{
 "cells": [
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.021928,
     "end_time": "2020-09-08T03:15:55.361283",
     "exception": false,
     "start_time": "2020-09-08T03:15:55.339355",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Predicting Loan Repayment\n",
    "\n",
    "\n",
    "The dataset for this project is retrieved from kaggle, the home of Data Science.\n",
    "\n",
    "The major aim of this project is to predict whether the customers will have their loan paid or not. Therefore, this is a supervised classification problem to be trained."
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.02001,
     "end_time": "2020-09-08T03:15:55.401984",
     "exception": false,
     "start_time": "2020-09-08T03:15:55.381974",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### **1- Importing Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:55.456123Z",
     "iopub.status.busy": "2020-09-08T03:15:55.455295Z",
     "iopub.status.idle": "2020-09-08T03:15:58.502367Z",
     "shell.execute_reply": "2020-09-08T03:15:58.501676Z"
    },
    "papermill": {
     "duration": 3.080197,
     "end_time": "2020-09-08T03:15:58.502515",
     "exception": false,
     "start_time": "2020-09-08T03:15:55.422318",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix, classification_report,accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.0203,
     "end_time": "2020-09-08T03:15:58.543518",
     "exception": false,
     "start_time": "2020-09-08T03:15:58.523218",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### **2- Getting Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:58.596255Z",
     "iopub.status.busy": "2020-09-08T03:15:58.594912Z",
     "iopub.status.idle": "2020-09-08T03:15:58.615955Z",
     "shell.execute_reply": "2020-09-08T03:15:58.616654Z"
    },
    "papermill": {
     "duration": 0.052871,
     "end_time": "2020-09-08T03:15:58.616870",
     "exception": false,
     "start_time": "2020-09-08T03:15:58.563999",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv('../input/loan-prediction-problem-dataset/train_u6lujuX_CVtuZ9i.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:58.702993Z",
     "iopub.status.busy": "2020-09-08T03:15:58.701710Z",
     "iopub.status.idle": "2020-09-08T03:15:58.726499Z",
     "shell.execute_reply": "2020-09-08T03:15:58.727470Z"
    },
    "papermill": {
     "duration": 0.08401,
     "end_time": "2020-09-08T03:15:58.727718",
     "exception": false,
     "start_time": "2020-09-08T03:15:58.643708",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:58.790142Z",
     "iopub.status.busy": "2020-09-08T03:15:58.789253Z",
     "iopub.status.idle": "2020-09-08T03:15:58.794164Z",
     "shell.execute_reply": "2020-09-08T03:15:58.793540Z"
    },
    "papermill": {
     "duration": 0.035154,
     "end_time": "2020-09-08T03:15:58.794299",
     "exception": false,
     "start_time": "2020-09-08T03:15:58.759145",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.02076,
     "end_time": "2020-09-08T03:15:58.836898",
     "exception": false,
     "start_time": "2020-09-08T03:15:58.816138",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### 2-1-Renaming columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:58.887370Z",
     "iopub.status.busy": "2020-09-08T03:15:58.886315Z",
     "iopub.status.idle": "2020-09-08T03:15:58.889371Z",
     "shell.execute_reply": "2020-09-08T03:15:58.889919Z"
    },
    "papermill": {
     "duration": 0.031593,
     "end_time": "2020-09-08T03:15:58.890108",
     "exception": false,
     "start_time": "2020-09-08T03:15:58.858515",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.columns=df.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:58.941867Z",
     "iopub.status.busy": "2020-09-08T03:15:58.941080Z",
     "iopub.status.idle": "2020-09-08T03:15:58.944879Z",
     "shell.execute_reply": "2020-09-08T03:15:58.944242Z"
    },
    "papermill": {
     "duration": 0.032546,
     "end_time": "2020-09-08T03:15:58.945013",
     "exception": false,
     "start_time": "2020-09-08T03:15:58.912467",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.columns=['loan_id', 'gender', 'married', 'dependents', 'education','self_employed', 'applicant_income', 'co-applicant_income', 'loan_amount', 'loan_amount_term', 'credit_history', 'property_area', 'loan_status']"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.0218,
     "end_time": "2020-09-08T03:15:58.989112",
     "exception": false,
     "start_time": "2020-09-08T03:15:58.967312",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### 2-2-Checking null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:59.043901Z",
     "iopub.status.busy": "2020-09-08T03:15:59.042741Z",
     "iopub.status.idle": "2020-09-08T03:15:59.048249Z",
     "shell.execute_reply": "2020-09-08T03:15:59.047450Z"
    },
    "papermill": {
     "duration": 0.036876,
     "end_time": "2020-09-08T03:15:59.048378",
     "exception": false,
     "start_time": "2020-09-08T03:15:59.011502",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_id                 0\n",
       "gender                 13\n",
       "married                 3\n",
       "dependents             15\n",
       "education               0\n",
       "self_employed          32\n",
       "applicant_income        0\n",
       "co-applicant_income     0\n",
       "loan_amount            22\n",
       "loan_amount_term       14\n",
       "credit_history         50\n",
       "property_area           0\n",
       "loan_status             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.021552,
     "end_time": "2020-09-08T03:15:59.091701",
     "exception": false,
     "start_time": "2020-09-08T03:15:59.070149",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "we take care of missing values in \"loan_amount\" and \"credit_history\".\n",
    "For other null values, we either delete a particular row if it has a null value for a particular feature and a particular column if it has more than 70-75% of missing values. This method is advised only when there are enough samples in the data set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:59.142140Z",
     "iopub.status.busy": "2020-09-08T03:15:59.141318Z",
     "iopub.status.idle": "2020-09-08T03:15:59.144823Z",
     "shell.execute_reply": "2020-09-08T03:15:59.144224Z"
    },
    "papermill": {
     "duration": 0.031739,
     "end_time": "2020-09-08T03:15:59.144958",
     "exception": false,
     "start_time": "2020-09-08T03:15:59.113219",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['loan_amount']=df['loan_amount'].fillna(df['loan_amount'].mean())   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:59.195412Z",
     "iopub.status.busy": "2020-09-08T03:15:59.194541Z",
     "iopub.status.idle": "2020-09-08T03:15:59.198957Z",
     "shell.execute_reply": "2020-09-08T03:15:59.198291Z"
    },
    "papermill": {
     "duration": 0.032463,
     "end_time": "2020-09-08T03:15:59.199089",
     "exception": false,
     "start_time": "2020-09-08T03:15:59.166626",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['credit_history']=df['credit_history'].fillna(df['credit_history'].median())   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:59.253265Z",
     "iopub.status.busy": "2020-09-08T03:15:59.252455Z",
     "iopub.status.idle": "2020-09-08T03:15:59.289669Z",
     "shell.execute_reply": "2020-09-08T03:15:59.288991Z"
    },
    "papermill": {
     "duration": 0.068028,
     "end_time": "2020-09-08T03:15:59.289838",
     "exception": false,
     "start_time": "2020-09-08T03:15:59.221810",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:59.344025Z",
     "iopub.status.busy": "2020-09-08T03:15:59.342998Z",
     "iopub.status.idle": "2020-09-08T03:15:59.347710Z",
     "shell.execute_reply": "2020-09-08T03:15:59.347105Z"
    },
    "papermill": {
     "duration": 0.035464,
     "end_time": "2020-09-08T03:15:59.347860",
     "exception": false,
     "start_time": "2020-09-08T03:15:59.312396",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_id                0\n",
       "gender                 0\n",
       "married                0\n",
       "dependents             0\n",
       "education              0\n",
       "self_employed          0\n",
       "applicant_income       0\n",
       "co-applicant_income    0\n",
       "loan_amount            0\n",
       "loan_amount_term       0\n",
       "credit_history         0\n",
       "property_area          0\n",
       "loan_status            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:59.416516Z",
     "iopub.status.busy": "2020-09-08T03:15:59.415574Z",
     "iopub.status.idle": "2020-09-08T03:15:59.419983Z",
     "shell.execute_reply": "2020-09-08T03:15:59.420509Z"
    },
    "papermill": {
     "duration": 0.050696,
     "end_time": "2020-09-08T03:15:59.420689",
     "exception": false,
     "start_time": "2020-09-08T03:15:59.369993",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>married</th>\n",
       "      <th>dependents</th>\n",
       "      <th>education</th>\n",
       "      <th>self_employed</th>\n",
       "      <th>applicant_income</th>\n",
       "      <th>co-applicant_income</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>loan_amount_term</th>\n",
       "      <th>credit_history</th>\n",
       "      <th>property_area</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    loan_id gender married dependents     education self_employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   applicant_income  co-applicant_income  loan_amount  loan_amount_term  \\\n",
       "0              5849                  0.0   146.412162             360.0   \n",
       "1              4583               1508.0   128.000000             360.0   \n",
       "2              3000                  0.0    66.000000             360.0   \n",
       "3              2583               2358.0   120.000000             360.0   \n",
       "4              6000                  0.0   141.000000             360.0   \n",
       "\n",
       "   credit_history property_area loan_status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:59.472812Z",
     "iopub.status.busy": "2020-09-08T03:15:59.471696Z",
     "iopub.status.idle": "2020-09-08T03:15:59.479183Z",
     "shell.execute_reply": "2020-09-08T03:15:59.478506Z"
    },
    "papermill": {
     "duration": 0.036258,
     "end_time": "2020-09-08T03:15:59.479328",
     "exception": false,
     "start_time": "2020-09-08T03:15:59.443070",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(542, 13)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:59.549428Z",
     "iopub.status.busy": "2020-09-08T03:15:59.548466Z",
     "iopub.status.idle": "2020-09-08T03:15:59.553767Z",
     "shell.execute_reply": "2020-09-08T03:15:59.552929Z"
    },
    "papermill": {
     "duration": 0.051843,
     "end_time": "2020-09-08T03:15:59.553962",
     "exception": false,
     "start_time": "2020-09-08T03:15:59.502119",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 542 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   loan_id              542 non-null    object \n",
      " 1   gender               542 non-null    object \n",
      " 2   married              542 non-null    object \n",
      " 3   dependents           542 non-null    object \n",
      " 4   education            542 non-null    object \n",
      " 5   self_employed        542 non-null    object \n",
      " 6   applicant_income     542 non-null    int64  \n",
      " 7   co-applicant_income  542 non-null    float64\n",
      " 8   loan_amount          542 non-null    float64\n",
      " 9   loan_amount_term     542 non-null    float64\n",
      " 10  credit_history       542 non-null    float64\n",
      " 11  property_area        542 non-null    object \n",
      " 12  loan_status          542 non-null    object \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 59.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:59.610508Z",
     "iopub.status.busy": "2020-09-08T03:15:59.609688Z",
     "iopub.status.idle": "2020-09-08T03:15:59.639407Z",
     "shell.execute_reply": "2020-09-08T03:15:59.638581Z"
    },
    "papermill": {
     "duration": 0.061133,
     "end_time": "2020-09-08T03:15:59.639535",
     "exception": false,
     "start_time": "2020-09-08T03:15:59.578402",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>applicant_income</th>\n",
       "      <th>co-applicant_income</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>loan_amount_term</th>\n",
       "      <th>credit_history</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>542.000000</td>\n",
       "      <td>542.000000</td>\n",
       "      <td>542.000000</td>\n",
       "      <td>542.000000</td>\n",
       "      <td>542.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5291.911439</td>\n",
       "      <td>1590.699114</td>\n",
       "      <td>145.518139</td>\n",
       "      <td>341.955720</td>\n",
       "      <td>0.863469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5506.461371</td>\n",
       "      <td>2543.801628</td>\n",
       "      <td>81.749385</td>\n",
       "      <td>65.441582</td>\n",
       "      <td>0.343669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2843.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3848.000000</td>\n",
       "      <td>1149.000000</td>\n",
       "      <td>129.500000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5820.500000</td>\n",
       "      <td>2281.000000</td>\n",
       "      <td>167.750000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>33837.000000</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       applicant_income  co-applicant_income  loan_amount  loan_amount_term  \\\n",
       "count        542.000000           542.000000   542.000000        542.000000   \n",
       "mean        5291.911439          1590.699114   145.518139        341.955720   \n",
       "std         5506.461371          2543.801628    81.749385         65.441582   \n",
       "min          150.000000             0.000000     9.000000         12.000000   \n",
       "25%         2843.750000             0.000000   100.000000        360.000000   \n",
       "50%         3848.000000          1149.000000   129.500000        360.000000   \n",
       "75%         5820.500000          2281.000000   167.750000        360.000000   \n",
       "max        81000.000000         33837.000000   650.000000        480.000000   \n",
       "\n",
       "       credit_history  \n",
       "count      542.000000  \n",
       "mean         0.863469  \n",
       "std          0.343669  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.024751,
     "end_time": "2020-09-08T03:15:59.689267",
     "exception": false,
     "start_time": "2020-09-08T03:15:59.664516",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### 2-3-Label Encoder for Dependents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:59.742308Z",
     "iopub.status.busy": "2020-09-08T03:15:59.741262Z",
     "iopub.status.idle": "2020-09-08T03:15:59.746797Z",
     "shell.execute_reply": "2020-09-08T03:15:59.746101Z"
    },
    "papermill": {
     "duration": 0.034653,
     "end_time": "2020-09-08T03:15:59.746933",
     "exception": false,
     "start_time": "2020-09-08T03:15:59.712280",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['dependents'].iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:59.801541Z",
     "iopub.status.busy": "2020-09-08T03:15:59.800397Z",
     "iopub.status.idle": "2020-09-08T03:15:59.804824Z",
     "shell.execute_reply": "2020-09-08T03:15:59.805505Z"
    },
    "papermill": {
     "duration": 0.035452,
     "end_time": "2020-09-08T03:15:59.805675",
     "exception": false,
     "start_time": "2020-09-08T03:15:59.770223",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '1', '2', '3+'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dependents'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:59.859345Z",
     "iopub.status.busy": "2020-09-08T03:15:59.858497Z",
     "iopub.status.idle": "2020-09-08T03:15:59.862150Z",
     "shell.execute_reply": "2020-09-08T03:15:59.861333Z"
    },
    "papermill": {
     "duration": 0.032559,
     "end_time": "2020-09-08T03:15:59.862281",
     "exception": false,
     "start_time": "2020-09-08T03:15:59.829722",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model6=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:59.915463Z",
     "iopub.status.busy": "2020-09-08T03:15:59.914653Z",
     "iopub.status.idle": "2020-09-08T03:15:59.921627Z",
     "shell.execute_reply": "2020-09-08T03:15:59.922176Z"
    },
    "papermill": {
     "duration": 0.036395,
     "end_time": "2020-09-08T03:15:59.922345",
     "exception": false,
     "start_time": "2020-09-08T03:15:59.885950",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model6.fit(df['dependents'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:15:59.976684Z",
     "iopub.status.busy": "2020-09-08T03:15:59.975944Z",
     "iopub.status.idle": "2020-09-08T03:15:59.979542Z",
     "shell.execute_reply": "2020-09-08T03:15:59.978814Z"
    },
    "papermill": {
     "duration": 0.033869,
     "end_time": "2020-09-08T03:15:59.979667",
     "exception": false,
     "start_time": "2020-09-08T03:15:59.945798",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['dependents']= model6.transform(df['dependents'])"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.02391,
     "end_time": "2020-09-08T03:16:00.027992",
     "exception": false,
     "start_time": "2020-09-08T03:16:00.004082",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3-Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.024019,
     "end_time": "2020-09-08T03:16:00.076251",
     "exception": false,
     "start_time": "2020-09-08T03:16:00.052232",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### 3-1- Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:00.133918Z",
     "iopub.status.busy": "2020-09-08T03:16:00.131797Z",
     "iopub.status.idle": "2020-09-08T03:16:00.138910Z",
     "shell.execute_reply": "2020-09-08T03:16:00.138164Z"
    },
    "papermill": {
     "duration": 0.038611,
     "end_time": "2020-09-08T03:16:00.139035",
     "exception": false,
     "start_time": "2020-09-08T03:16:00.100424",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "376"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['loan_status']=='Y'].count()['loan_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:00.197358Z",
     "iopub.status.busy": "2020-09-08T03:16:00.196382Z",
     "iopub.status.idle": "2020-09-08T03:16:00.201381Z",
     "shell.execute_reply": "2020-09-08T03:16:00.201964Z"
    },
    "papermill": {
     "duration": 0.038971,
     "end_time": "2020-09-08T03:16:00.202132",
     "exception": false,
     "start_time": "2020-09-08T03:16:00.163161",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "166"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['loan_status']=='N'].count()['loan_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:00.258249Z",
     "iopub.status.busy": "2020-09-08T03:16:00.257475Z",
     "iopub.status.idle": "2020-09-08T03:16:00.424512Z",
     "shell.execute_reply": "2020-09-08T03:16:00.423608Z"
    },
    "papermill": {
     "duration": 0.198042,
     "end_time": "2020-09-08T03:16:00.424659",
     "exception": false,
     "start_time": "2020-09-08T03:16:00.226617",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Loan Status')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.pie(x=[376,166], labels=['Yes','No'], autopct='%1.0f%%', pctdistance=0.5,labeldistance=0.7,colors=['g','r'])\n",
    "plt.title('Distribution of Loan Status')"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.027202,
     "end_time": "2020-09-08T03:16:00.477382",
     "exception": false,
     "start_time": "2020-09-08T03:16:00.450180",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "69% of applicants repay the loan and 39% do not repay the loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:00.551383Z",
     "iopub.status.busy": "2020-09-08T03:16:00.547949Z",
     "iopub.status.idle": "2020-09-08T03:16:01.327763Z",
     "shell.execute_reply": "2020-09-08T03:16:01.328440Z"
    },
    "papermill": {
     "duration": 0.823691,
     "end_time": "2020-09-08T03:16:01.328675",
     "exception": false,
     "start_time": "2020-09-08T03:16:00.504984",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([], <a list of 0 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "\n",
    "plt.subplot(2,3,1)\n",
    "sns.countplot(x='gender' ,hue='loan_status', data=df,palette='plasma')\n",
    "\n",
    "plt.subplot(2,3,2)\n",
    "sns.countplot(x='married',hue='loan_status',data=df,palette='viridis')\n",
    "plt.ylabel(' ')\n",
    "plt.yticks([ ])\n",
    "\n",
    "plt.subplot(2,3,3)\n",
    "sns.countplot(x='education',hue='loan_status',data=df,palette='copper')\n",
    "plt.ylabel(' ')\n",
    "plt.yticks([ ])\n",
    "\n",
    "plt.subplot(2,3,4)\n",
    "sns.countplot(x='credit_history', data=df,hue='loan_status',palette='summer')\n",
    "\n",
    "plt.subplot(2,3,5)\n",
    "sns.countplot(x='self_employed',hue='loan_status',data=df,palette='autumn')\n",
    "plt.ylabel(' ')\n",
    "plt.yticks([ ])\n",
    "\n",
    "plt.subplot(2,3,6)\n",
    "sns.countplot(x='property_area',data=df,hue='loan_status',palette='PuBuGn')\n",
    "plt.ylabel(' ')\n",
    "plt.yticks([ ])"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.025504,
     "end_time": "2020-09-08T03:16:01.381656",
     "exception": false,
     "start_time": "2020-09-08T03:16:01.356152",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Comparison between Genders in getting the Loan shows that a Male Individual has more chance of repaying the Loan.\n",
    "\n",
    "Comparison between Married Status in getting the Loan shows that a Married Individual has more chance of repaying the Loan.\n",
    "   \n",
    "Comparison between Education Status of an Individual in getting the Loan shows that a Graduate Individual has more chance of repaying the Loan.\n",
    "    \n",
    "Comparison between Self-Employed or Not in getting the Loan shows that Not Self-Employed has more chance of repaying the Loan.\n",
    "\n",
    "Comparison between Credit History for getting the Loan shows that an individual with a credit history has more chance of repaying the Loan.\n",
    "    \n",
    "Comparison between Property Area for getting the Loan shows that People living in Semi-Urban Area have more chance to repay the Loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:01.441150Z",
     "iopub.status.busy": "2020-09-08T03:16:01.440328Z",
     "iopub.status.idle": "2020-09-08T03:16:03.154754Z",
     "shell.execute_reply": "2020-09-08T03:16:03.153921Z"
    },
    "papermill": {
     "duration": 1.7457,
     "end_time": "2020-09-08T03:16:03.154913",
     "exception": false,
     "start_time": "2020-09-08T03:16:01.409213",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
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       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
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       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "            <script type=\"text/javascript\">\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.sunburst( data_frame=df,path=['gender','loan_status'], color='loan_amount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:03.284219Z",
     "iopub.status.busy": "2020-09-08T03:16:03.282805Z",
     "iopub.status.idle": "2020-09-08T03:16:04.624134Z",
     "shell.execute_reply": "2020-09-08T03:16:04.623223Z"
    },
    "papermill": {
     "duration": 1.392117,
     "end_time": "2020-09-08T03:16:04.624290",
     "exception": false,
     "start_time": "2020-09-08T03:16:03.232173",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([], <a list of 0 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "\n",
    "plt.subplot(2,3,1)\n",
    "sns.violinplot(x='gender', y='loan_amount',hue='loan_status', data=df,palette='plasma')\n",
    "\n",
    "plt.subplot(2,3,2)\n",
    "sns.violinplot(x='married',y='loan_amount',hue='loan_status',data=df,palette='viridis')\n",
    "plt.ylabel(' ')\n",
    "plt.yticks([ ])\n",
    "\n",
    "plt.subplot(2,3,3)\n",
    "sns.violinplot(x='education',y='loan_amount',hue='loan_status',data=df,palette='copper')\n",
    "plt.ylabel(' ')\n",
    "plt.yticks([ ])\n",
    "\n",
    "plt.subplot(2,3,4)\n",
    "sns.violinplot(x='credit_history',y='loan_amount', data=df,hue='loan_status',palette='summer')\n",
    "\n",
    "plt.subplot(2,3,5)\n",
    "sns.violinplot(x='self_employed',y='loan_amount',hue='loan_status',data=df,palette='autumn')\n",
    "plt.ylabel(' ')\n",
    "plt.yticks([ ])\n",
    "\n",
    "plt.subplot(2,3,6)\n",
    "sns.violinplot(x='property_area', y='loan_amount',data=df,hue='loan_status',palette='PuBuGn')\n",
    "plt.ylabel(' ')\n",
    "plt.yticks([ ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:04.708238Z",
     "iopub.status.busy": "2020-09-08T03:16:04.703360Z",
     "iopub.status.idle": "2020-09-08T03:16:05.525060Z",
     "shell.execute_reply": "2020-09-08T03:16:05.525759Z"
    },
    "papermill": {
     "duration": 0.869293,
     "end_time": "2020-09-08T03:16:05.525969",
     "exception": false,
     "start_time": "2020-09-08T03:16:04.656676",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd866681610>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,5))\n",
    "\n",
    "\n",
    "plt.subplot(1,3,1)\n",
    "sns.distplot(df['applicant_income'],bins=30,color='r',hist_kws=dict(edgecolor='white'))\n",
    "plt.ylabel('frequency')\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "sns.distplot(df['co-applicant_income'],bins=30,color='blue',hist_kws=dict(edgecolor='white'))\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "sns.distplot(df['loan_amount'],bins=30,color='black',hist_kws=dict(edgecolor='white'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:05.598242Z",
     "iopub.status.busy": "2020-09-08T03:16:05.597077Z",
     "iopub.status.idle": "2020-09-08T03:16:05.764662Z",
     "shell.execute_reply": "2020-09-08T03:16:05.763869Z"
    },
    "papermill": {
     "duration": 0.206593,
     "end_time": "2020-09-08T03:16:05.764820",
     "exception": false,
     "start_time": "2020-09-08T03:16:05.558227",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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[0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('2060c050-a50b-477c-8cb9-650bd950b35d');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.scatter_3d(data_frame=df,x='applicant_income',y='co-applicant_income',z='loan_amount',color='loan_status')"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.02967,
     "end_time": "2020-09-08T03:16:05.823612",
     "exception": false,
     "start_time": "2020-09-08T03:16:05.793942",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### 3-2-Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.032563,
     "end_time": "2020-09-08T03:16:05.885144",
     "exception": false,
     "start_time": "2020-09-08T03:16:05.852581",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "###### 3-2-1-gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:05.949285Z",
     "iopub.status.busy": "2020-09-08T03:16:05.948498Z",
     "iopub.status.idle": "2020-09-08T03:16:05.952191Z",
     "shell.execute_reply": "2020-09-08T03:16:05.951396Z"
    },
    "papermill": {
     "duration": 0.03787,
     "end_time": "2020-09-08T03:16:05.952327",
     "exception": false,
     "start_time": "2020-09-08T03:16:05.914457",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model1=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:06.019197Z",
     "iopub.status.busy": "2020-09-08T03:16:06.018016Z",
     "iopub.status.idle": "2020-09-08T03:16:06.023852Z",
     "shell.execute_reply": "2020-09-08T03:16:06.023070Z"
    },
    "papermill": {
     "duration": 0.042876,
     "end_time": "2020-09-08T03:16:06.023983",
     "exception": false,
     "start_time": "2020-09-08T03:16:05.981107",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.fit(df['gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:06.094072Z",
     "iopub.status.busy": "2020-09-08T03:16:06.093112Z",
     "iopub.status.idle": "2020-09-08T03:16:06.096744Z",
     "shell.execute_reply": "2020-09-08T03:16:06.096070Z"
    },
    "papermill": {
     "duration": 0.043284,
     "end_time": "2020-09-08T03:16:06.096910",
     "exception": false,
     "start_time": "2020-09-08T03:16:06.053626",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['gender']= model1.transform(df['gender'])"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.029729,
     "end_time": "2020-09-08T03:16:06.155847",
     "exception": false,
     "start_time": "2020-09-08T03:16:06.126118",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "###### 3-2-2-married"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:06.222196Z",
     "iopub.status.busy": "2020-09-08T03:16:06.221004Z",
     "iopub.status.idle": "2020-09-08T03:16:06.224732Z",
     "shell.execute_reply": "2020-09-08T03:16:06.223978Z"
    },
    "papermill": {
     "duration": 0.04018,
     "end_time": "2020-09-08T03:16:06.224884",
     "exception": false,
     "start_time": "2020-09-08T03:16:06.184704",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model2=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:06.295821Z",
     "iopub.status.busy": "2020-09-08T03:16:06.294988Z",
     "iopub.status.idle": "2020-09-08T03:16:06.299327Z",
     "shell.execute_reply": "2020-09-08T03:16:06.298561Z"
    },
    "papermill": {
     "duration": 0.044075,
     "end_time": "2020-09-08T03:16:06.299454",
     "exception": false,
     "start_time": "2020-09-08T03:16:06.255379",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.fit(df['married'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:06.367967Z",
     "iopub.status.busy": "2020-09-08T03:16:06.366916Z",
     "iopub.status.idle": "2020-09-08T03:16:06.370583Z",
     "shell.execute_reply": "2020-09-08T03:16:06.369948Z"
    },
    "papermill": {
     "duration": 0.040351,
     "end_time": "2020-09-08T03:16:06.370713",
     "exception": false,
     "start_time": "2020-09-08T03:16:06.330362",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['married']= model2.transform(df['married'])"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.031241,
     "end_time": "2020-09-08T03:16:06.432897",
     "exception": false,
     "start_time": "2020-09-08T03:16:06.401656",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "###### 3-2-3-education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:06.504427Z",
     "iopub.status.busy": "2020-09-08T03:16:06.503566Z",
     "iopub.status.idle": "2020-09-08T03:16:06.507525Z",
     "shell.execute_reply": "2020-09-08T03:16:06.506682Z"
    },
    "papermill": {
     "duration": 0.041005,
     "end_time": "2020-09-08T03:16:06.507659",
     "exception": false,
     "start_time": "2020-09-08T03:16:06.466654",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model3=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:06.574611Z",
     "iopub.status.busy": "2020-09-08T03:16:06.573448Z",
     "iopub.status.idle": "2020-09-08T03:16:06.578526Z",
     "shell.execute_reply": "2020-09-08T03:16:06.577945Z"
    },
    "papermill": {
     "duration": 0.041678,
     "end_time": "2020-09-08T03:16:06.578667",
     "exception": false,
     "start_time": "2020-09-08T03:16:06.536989",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.fit(df['education'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:06.647661Z",
     "iopub.status.busy": "2020-09-08T03:16:06.646476Z",
     "iopub.status.idle": "2020-09-08T03:16:06.649387Z",
     "shell.execute_reply": "2020-09-08T03:16:06.649995Z"
    },
    "papermill": {
     "duration": 0.041559,
     "end_time": "2020-09-08T03:16:06.650166",
     "exception": false,
     "start_time": "2020-09-08T03:16:06.608607",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['education']= model3.transform(df['education'])"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.029583,
     "end_time": "2020-09-08T03:16:06.709183",
     "exception": false,
     "start_time": "2020-09-08T03:16:06.679600",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "###### 3-2-4-self_employed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:06.775569Z",
     "iopub.status.busy": "2020-09-08T03:16:06.774514Z",
     "iopub.status.idle": "2020-09-08T03:16:06.777409Z",
     "shell.execute_reply": "2020-09-08T03:16:06.777964Z"
    },
    "papermill": {
     "duration": 0.039598,
     "end_time": "2020-09-08T03:16:06.778140",
     "exception": false,
     "start_time": "2020-09-08T03:16:06.738542",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model4=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:06.846303Z",
     "iopub.status.busy": "2020-09-08T03:16:06.845098Z",
     "iopub.status.idle": "2020-09-08T03:16:06.850215Z",
     "shell.execute_reply": "2020-09-08T03:16:06.849498Z"
    },
    "papermill": {
     "duration": 0.042008,
     "end_time": "2020-09-08T03:16:06.850339",
     "exception": false,
     "start_time": "2020-09-08T03:16:06.808331",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4.fit(df['self_employed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:06.917673Z",
     "iopub.status.busy": "2020-09-08T03:16:06.916924Z",
     "iopub.status.idle": "2020-09-08T03:16:06.920588Z",
     "shell.execute_reply": "2020-09-08T03:16:06.919849Z"
    },
    "papermill": {
     "duration": 0.040144,
     "end_time": "2020-09-08T03:16:06.920711",
     "exception": false,
     "start_time": "2020-09-08T03:16:06.880567",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['self_employed']= model4.transform(df['self_employed'])"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.029232,
     "end_time": "2020-09-08T03:16:06.979610",
     "exception": false,
     "start_time": "2020-09-08T03:16:06.950378",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "###### 3-2-5-property_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:07.049537Z",
     "iopub.status.busy": "2020-09-08T03:16:07.048731Z",
     "iopub.status.idle": "2020-09-08T03:16:07.052665Z",
     "shell.execute_reply": "2020-09-08T03:16:07.051866Z"
    },
    "papermill": {
     "duration": 0.040401,
     "end_time": "2020-09-08T03:16:07.052818",
     "exception": false,
     "start_time": "2020-09-08T03:16:07.012417",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model5=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:07.122699Z",
     "iopub.status.busy": "2020-09-08T03:16:07.121879Z",
     "iopub.status.idle": "2020-09-08T03:16:07.126525Z",
     "shell.execute_reply": "2020-09-08T03:16:07.125827Z"
    },
    "papermill": {
     "duration": 0.042427,
     "end_time": "2020-09-08T03:16:07.126658",
     "exception": false,
     "start_time": "2020-09-08T03:16:07.084231",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model5.fit(df['property_area'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:07.194181Z",
     "iopub.status.busy": "2020-09-08T03:16:07.193280Z",
     "iopub.status.idle": "2020-09-08T03:16:07.196891Z",
     "shell.execute_reply": "2020-09-08T03:16:07.196151Z"
    },
    "papermill": {
     "duration": 0.040125,
     "end_time": "2020-09-08T03:16:07.197050",
     "exception": false,
     "start_time": "2020-09-08T03:16:07.156925",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['property_area']= model5.transform(df['property_area'])"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.030197,
     "end_time": "2020-09-08T03:16:07.259319",
     "exception": false,
     "start_time": "2020-09-08T03:16:07.229122",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "###### 3-2-6-loan status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:07.328214Z",
     "iopub.status.busy": "2020-09-08T03:16:07.327234Z",
     "iopub.status.idle": "2020-09-08T03:16:07.330547Z",
     "shell.execute_reply": "2020-09-08T03:16:07.329812Z"
    },
    "papermill": {
     "duration": 0.039562,
     "end_time": "2020-09-08T03:16:07.330672",
     "exception": false,
     "start_time": "2020-09-08T03:16:07.291110",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model6=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:07.399955Z",
     "iopub.status.busy": "2020-09-08T03:16:07.398962Z",
     "iopub.status.idle": "2020-09-08T03:16:07.403168Z",
     "shell.execute_reply": "2020-09-08T03:16:07.403831Z"
    },
    "papermill": {
     "duration": 0.043225,
     "end_time": "2020-09-08T03:16:07.403994",
     "exception": false,
     "start_time": "2020-09-08T03:16:07.360769",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model6.fit(df['loan_status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:07.474101Z",
     "iopub.status.busy": "2020-09-08T03:16:07.472978Z",
     "iopub.status.idle": "2020-09-08T03:16:07.476673Z",
     "shell.execute_reply": "2020-09-08T03:16:07.475890Z"
    },
    "papermill": {
     "duration": 0.042488,
     "end_time": "2020-09-08T03:16:07.476824",
     "exception": false,
     "start_time": "2020-09-08T03:16:07.434336",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['loan_status']= model6.transform(df['loan_status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:07.562874Z",
     "iopub.status.busy": "2020-09-08T03:16:07.561806Z",
     "iopub.status.idle": "2020-09-08T03:16:07.568836Z",
     "shell.execute_reply": "2020-09-08T03:16:07.568038Z"
    },
    "papermill": {
     "duration": 0.059515,
     "end_time": "2020-09-08T03:16:07.568966",
     "exception": false,
     "start_time": "2020-09-08T03:16:07.509451",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>married</th>\n",
       "      <th>dependents</th>\n",
       "      <th>education</th>\n",
       "      <th>self_employed</th>\n",
       "      <th>applicant_income</th>\n",
       "      <th>co-applicant_income</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>loan_amount_term</th>\n",
       "      <th>credit_history</th>\n",
       "      <th>property_area</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    loan_id  gender  married  dependents  education  self_employed  \\\n",
       "0  LP001002       1        0           0          0              0   \n",
       "1  LP001003       1        1           1          0              0   \n",
       "2  LP001005       1        1           0          0              1   \n",
       "3  LP001006       1        1           0          1              0   \n",
       "4  LP001008       1        0           0          0              0   \n",
       "\n",
       "   applicant_income  co-applicant_income  loan_amount  loan_amount_term  \\\n",
       "0              5849                  0.0   146.412162             360.0   \n",
       "1              4583               1508.0   128.000000             360.0   \n",
       "2              3000                  0.0    66.000000             360.0   \n",
       "3              2583               2358.0   120.000000             360.0   \n",
       "4              6000                  0.0   141.000000             360.0   \n",
       "\n",
       "   credit_history  property_area  loan_status  \n",
       "0             1.0              2            1  \n",
       "1             1.0              0            0  \n",
       "2             1.0              2            1  \n",
       "3             1.0              2            1  \n",
       "4             1.0              2            1  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:07.647936Z",
     "iopub.status.busy": "2020-09-08T03:16:07.646703Z",
     "iopub.status.idle": "2020-09-08T03:16:08.549734Z",
     "shell.execute_reply": "2020-09-08T03:16:08.548510Z"
    },
    "papermill": {
     "duration": 0.949081,
     "end_time": "2020-09-08T03:16:08.549911",
     "exception": false,
     "start_time": "2020-09-08T03:16:07.600830",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation Matrix for Loan Status')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MmgCfAd8CRwDTgNeA+4C6wIVh02eAvYAtwGXu7mbWGzgJqARUMbPXc3y/nKBq18bMEoBHgGOBisCz7v6imcUBQ4BuwJ+w63PH6yVWZcWGlKzvK1I2cnCD+rna9bBm3NS1CzWrVKbP2x9lrY8Ar15wJhHgnV9n8M5vM3Y1JBERkRIpPaJHtpQ1BwCDgbZAS+ACoAtwM3AHMAc42t0PBe4GHoratxNwqbt3i/E90xXAendvD7QHrjKzpsAZgAEHAVcBnXf1ZOLy+PObVyVvvC+g1wvD6ffeJ1x/7PbDnj/sHc545U2ufGskF7Y7mHaNG+5qSCIiIrKbqdJXMH+6+wwAM5sJTHD3iJnNAJoA1YHhZtacoBBWPmrf8e6+Jp/vmY4H2ppZ5nBvdaA5cDTwlrunA8vMbOKunsyKDRupXy0x63v9xKqsStkUs/3Pfy1l3xrVqbFXJdZuSWXVxqDtms1bGO/zadugPj//tXRXwxIRESlx9MiWsmdr1HJG1PcMgsT5AeBLd28DnEIwfJspZzYVK7uKAwa6+yHhp6m7fx5uK9Ib6mYsW0GTmjVotHc1ysfHc9KBxoS5f2Rr07hG9azl1vXrUiE+gbVbUtmrfDmqVAhy2r3Kl+PIpvsxb1VyUYYnIiJSYmRE4ov8U1xU6Ssa1YHMUlfvnexjHHCNmU10921m1iLscxJwdXg/YF2gK7BLz0hJj0S4/7OJvHL+mSTEx/H+1JnMT17NeYe1BeDtX6fTs2VzTm/bmrT0dFLT0rh+5KcA1K5ShWfPPgWAhPh4Rv0+h2/+WLQr4YiIiMg/QElf0XiMYHj3RmBnh19fJhgq/jWcvJEEnA6MJJjEMQOYC3y9y9ECXy9YyNfPD8u27u1fp2ctvzT5Z16a/HOu/RavW8+pL40oihBERERKvNI0vBsXiehRHKVQBKDFg08XdxzMHXRDcYcgIiJ7phIxbfbN+R2LPFG64IAfi+XcVOkTERERiUGPbBERERGRPYoqfSIiIiIxlKbXsCnpExEREYkhvRgfsVLUSs+ZiIiIiEhMqvSJiIiIxJBRMiYRFwlV+kRERETKAFX6RERERGIoTff0KekTERERiaE0vZGj9JyJiIiIiMSkSp+IiIhIDBml6I0cSvpKMb33VkREZM9kZr2AwUAC8LK7P5Jje3VgBNCYIJ97wt1fy69PDe+KiIiIxJBOfJF/dsTMEoBngROA1sD5ZtY6R7P+wCx3Pxg4FnjSzCrk168qfSIiIiIxZBTP7N0OwHx3/wPAzN4GTgNmRbWJAIlmFgdUBdYAafl1qqSvFGt35VPFHQI/v3xj1nKLh54uxkgCc+/QkLeIiJR4DYHFUd+XAB1ztPkv8AmwDEgEznX3jPw6VdInIiIiEkP6bngjh5n1AfpErRrq7kOjvud10EiO7z2BqUA3oBkw3sy+cfcNsY6rpE9ERETkHxQmeEPzabIE2DfqeyOCil60y4BH3D0CzDezP4GWwE+xOlXSJyIiIhJDMd3TNwVobmZNgaXAecAFOdr8BRwHfGNm9QAD/sivU83eFRERESlB3D0NGACMA2YD77r7TDPra2Z9w2YPAJ3NbAYwAbjV3ZPz61eVPhEREZEYdsc9fQXh7mOAMTnWvRC1vAw4vjB9KukTERERiaGYhnd3i9JzJiIiIiISkyp9IiIiIjGkq9InIiIiInsSVfpEREREYsgopokcu4OSPhEREZEYStPwrpI+odOBTbj5/GOJj4/no29mMHzslGzbe3VsyaUntAdgc+o2HhnxBfOW5PsooEI7av/9uLPHsSTExfPetN8ZOjl7DMc135/rjulMJBIhLSPCQ+O/4pclwcPJEytW5N8n9aBFnVpEIhFu/3Q8U5cuL9L4RERE9nSlOukzs3uBje7+xD983GOBm9395J3c/w53f6hoo8pbfFwct17Yjf5PfcDKtSm8PuhCJk1dwJ/L12S1WZa8nj6PvUvK5q10btOEOy/pQe+H3irSGO7p2Y3L3vqQFRtS+OCyC5gwbwELkrfHMHnhYibMGwGA1anN4DNPoteLwwEY1ONYvlmwkGs/HE35+HgqlS9fZLGJiEjZlhEpPcO7padmWbrc8U8d6MCm9Vm8ah1Lk9eTlp7B5z/N4ZhDmmVrM33BclI2bwVgxh/LqVsjsUhjaNugPovWrmPxuvVsy8jg01lO9+bZY9i8bVvW8l4VyhOJBO+drlKhAu0aN+S9ab8DsC0jg5StW4s0PhERkdKg1FX6zOxO4BJgMZAE/GJmzYBngTrAZuAqd59jZsOAVOBAoB5wo7uPNrME4BHgWKAi8Ky7vxhW8O4FkoE2wC/ARe4eMbNewDPhtl+j4qkCDAEOIrje97r7x2bWGzgVqAw0A0a6+y1m9giwl5lNBWYCfYB3CV62nAA84O7vFNX1qlujKivXpmR9X7V2I2323ydm+9O6tOH73/8sqsMDUC+xKis2bI9hRcpGDm5QP1e7Hi2acVPXLtSsXJk+734EQOO9q7N28xYeOfl4Wtatw8wVK3lw/Fds2ZZWpDGKiEjZlF6K6mOl50wAMzuc4KXEhwJnAu3DTUOBge5+OHAz8FzUbk2AY4CTgBfMrBJwBbDe3duHfVwVvvSYsO/rgdbA/sCR4T4vAacARwHRGcudwMSwr67A42EiCHAIcC5BQniume3r7rcBW9z9EHe/EOgFLHP3g929DfDZrl6nHcmsouV0uO3LaUe1Ycj73xTp8fIqnEfIHcP4uQvo9eJw+r3/Cdcf3RmAhPh4Wtevy5u/Tuf0V//H5m1p9OnUPte+IiIiOyMjElfkn+JS2ip9RxFUzDYDmNknQCWgM/CemWW2qxi1z7vungHMM7M/gJYE77Jra2b/CttUB5oDfwM/ufuSsP+pBEnjRuBPd58Xrh9BUKEj7OtUM7s5/F4JaBwuT3D39eE+s4D9CCqU0WYAT5jZo8Body/SjGvV2o3UixqurVujKknrNuZqd0Cj2tx1aQ+uHfwh6zelFmUIrEjZSP1q22Oon1iVVSmbYrb/efFS9q1RnRp7VWJFSgorNqQwfdkKAMbNmUefTu2KND4REZHSoLQlfUCuElE8sM7dDylg+whB8Wmgu4+L3hAO70bfMJbO9muYd3ks6Ossd/ccfXXMp68s7j43rGCeCDxsZp+7+/0xjlVosxauYN96e9OgdjVWrd3I8R1aMuilbO93pl7NRB7vdyp3vzKWv1auK6pDZ5mxbAVNatSgUfVqrEzZyEmtjRs/HputTeMa1flr7XoAWterS4WEBNZuCZLPFSkbaVqzBn+uWUunJvsyP2oCiIiIyK7IKEWDoqUt6ZsEDAvviytHMNz6IvCnmZ3t7u+ZWRzQ1t2nhfucbWbDgaYEw7UOjAOuMbOJ7r7NzFoAS/M57hygqZk1c/cFwPlR28YBA81sYHjv36Hu/tsOzmObmZUPj90AWOPuI8xsI9C7MBdkR9IzIjz+5pcMuf4sEuLj+OS73/lj2WrOOqYtAB98PZ2rTjmC6lUqceuFx4X7ZHDJg28WXQyRCPd/PpFXzjuThPg43p82k/nJqznv0CCGt3+bTk9rzukHtSYtI53UbWlcP/LTrP0fGPclT5x2AuUT4lmydj23ffp5kcUmIiJSWpSqpM/dfzWzd4CpwCIgcyj0QuB5MxsElAfeBjKTPge+JpjI0dfdU83sZYJh21/DJDEJOD2f46aaWR/gUzNLBr4lmOgB8ADBBI/pYV8LgR09ymVo2P5X4HWC+wAzgG3ANQW5FoXx3Yw/+W5G9skZH3w9PWv5weHjeXD4+KI+bDZfL1jI1wuGZVv39m/bY3jph5956Yef89x39qokznqt6JJQERGRTOml6JEtcbFu2i8Lwtm7o939/eKOpYhFANpd+VRxx8HPL9+YtdzioaeLMZLA3DtuKO4QRESkYEpEtnXD1POKPFF6+pC3i+XcSlWlT0RERKQolaaHM5fppM/dexd3DCIiIlJyZZSid++WnjMRERERkZjKdKVPREREJD/pJePWwiKhSp+IiIhIGaBKn4iIiEgMmsghIiIiUgZoIoeIiIiI7FFU6RMRERGJIUMTOURERERkT6JKn4iIiEgMeveulHT6UUVEZE9XIrKtS3+6osj/Th3e4ZViOTcN74qIiIiUARreFREREYlBz+mTPcJx3R4u7hCYMPH2rOWe7e8rxkgC46bck7XcrccjxRhJYOL424o7BBERKSOU9ImIiIjEoEe2iIiIiMgeRZU+ERERkRh0T5+IiIhIGaB374qIiIjIHkWVPhEREZEYStPwrip9IiIiImWAKn0iIiIiMZSmR7Yo6RMRERGJQcO7IiIiIrJHUaVPREREJIbSVOlT0ldGtW+/P/0HdCc+Pp4xY6by9ls/5GrTf0APOnZsxtbUbTz22GjmzVsJQJUqFbn55hNp0rQOkUiEJx4fw6xZSwsdQ7tOzeh7Uy8S4uMZ+/GvvDv8u1xtrrmpFx2ObE5q6jaevO8j5vsKAG6861Q6dmnBurWbuPq857PaX9K3K52ONiKRCOvWbOKJ+z5iTfLGgl2Tdk0Z0C+8JmOn8dY7ua/JgH7d6dihGalbt/HY458yb/5K9m1Uk7sGnZbVZp/6ezNs+Dd8MPJnjjnauPTiLjRuXJt+A4czd+6Kwl4mERGRIqHh3RjMrLeZ/beI+zzdzFpHfb/fzLoX5TEKIj4+jmuvO57bb3uXyy8bSrdurdlvv1rZ2nTo2IxGDWtwycUv8NRTY7nu+l5Z2wYM6MGUKX9wWe+h9LnqFRYtSt6pGPrfciKDrvsfV53zLF2Pb0PjprWztWnf+QAaNq7JZWcOYfBDoxh420lZ2z4fPZU7rx2Rq9/33/iOay54gX4XvsiP387loiuPKXA81w08ntvueJfLrnyJbl1bs1/j7NekY4f9adiwBhf3fpGnnvmM66/tCcDiJWvo0/c1+vR9jb79hrF16za+/W4uAH8uTOae+0YyfcbiQl0fEREpGTIicUX+KS5K+v5ZpwNZSZ+73+3uX/zTQbRs2YClS9eyfPk60tIy+HLibDp3bpGtzZGdm/P5+N8BmD17GVWrVqRmzSpUrlyBg9ruy5gx0wBIS8tg06athY7BDmzIssVrWLE0iOGr8TPpdEzLbG06HdOSLz6dDsCc35dSJbESNWtVBeD33/4iZcOWXP1u3vR31nKlvSoQiRQsnpa2D0uXrWX5ivWkpWUw8atZdO7cPFubzp2aM/6L3Nck2mGH7sey5etYuWoDAH/9tZrFS9YULAgRESlxSlPSV2aHd83sIuBaoALwI9APuAS4HVgOzAW2hm2HAaPd/f3w+0Z3rxou3wJcDGQAY939NjO7CugT9j0/3H4IcCpwjJkNAs4C7srs18yOA54g+E2mANe4+1YzWwgMB04BygNnu/ucXTn32rWrkhQmJQBJySm0atUgR5vE7G2SUqhdO5H09AzWr9/MLbecxP7N6jJv7gqeffYLUlO3FSqGWnUSSVq5vf/klRto2aZh9hjqJJK0cv32Nqs2UKtuImtW5z9c2/uabnQ/qS2bNm7llr7DCxRP7dqJrEpK2X6s5BRatcx9TVat2t4mKTm4JmvWbMpa1/XY1kz8claBjikiIvJPKpOVPjNrBZwLHOnuhwDpwEXAfcCRQA+iKnL59HMCQfWuo7sfDDwWbvrQ3duH62YDV7j798AnwP+5+yHuviCqn0rAMOBcdz+IIPG7JupQye5+GPA8cPPOn3koLve/MiI5S2J5/EMkEoGEhHiaN6/PJ5/8Rt+rXyM1dRvnnd9pJ0LIK4acjfKOYUeGPT+Ri05+homfzeDUczoUMJ4dHyvvNtsblSsXT+dOB/D117uUk4uISAmSQVyRf4pLmUz6gOOAw4EpZjY1/H4D8JW7J7n738A7BeinO/Cau28GcPfMcbw2ZvaNmc0ALgQO3EE/Bvzp7nPD78OBo6O2fxj+9xegSQHiyldyUgp16lbL+l6ndiKrc0x2yNWmTiKrV6eQlJRCUtIG5sxZBsCkSXNo3rxe4WNYtYE69bb3X7teNVYnp+Rok0KdetW3t6lbjTVJ2dvk58vPZtClW6sCtU1KSqFuncTtx6qdSPLqlNxt6m5vU6d2Iqujqo4d2jdj3vyVrF23udtgdQ8AACAASURBVMAxioiI/FPKatIXBwwPK26HuLsB9wKx6khphNfKzOIIhm0z+8lrn2HAgLBqdx9QqQDx5Cfzprl0imBIfs6cZTRsWIP69atTrlw8Xbu14vvJ87K1+f77eRzfow0ArVo1YNOmraxZs4m1azeRtCqFRvvWBODQw5rs1EQOn7WUho1rUa/B3pQrF8+xPQ7kh0merc0Pk5zuJ7UFoGWbhmzeuHWHQ7sNwrgAjjjaWLywYLHN8eU0bFgz65p0O7Y1kyfPz9bm+8nz6dE99zXJ1K1rKw3tioiUMrqnb883AfjYzJ5291VmVhP4DRhsZrWADcDZwLSw/UKCyuC7wGkE99YBfA7cbWZvuvtmM6sZVvsSgeVmVp6g0pf5PJOUcFtOc4AmZnaAu2feA/h10Z7ydhkZEYYMGc+jj55HfEIcY8dOZ9HCZE4+5VAARo/6jR9/XEDHjs14Y0RfUlO38fhjn2btP2TI59xxx6mUL5fA8uXreCxqW4FjSI/w7GNjeOg/FxGfEMfnn0xl0R9JnHTm4QB8+uEv/PTdPNof2ZzXRg5ka+o2nrz/46z9b3vwTNoe3oTqe1dmxOgbeGPoV4z75DeuGHAcjfarTUZGhFUr1vGfhwsWW0ZGhCH//ZxHHz6XhPg4xo6bzsJFyZxy8iEAjBo9lR9/WkDHjvszYvjVwSNbnhiTtX/FiuU4/PCmPP3MuGz9djmyBQP7d6d69co89ODZLFiwkltvf7fQ10tERGRXxeW6l6uMMLNzCSZtxAPbgP5AK7ZP5JgKJLj7ADOrB3wctp0ADIyayHEbwQSQv4Ex7n6HmV0D3AIsAmYAie7e28yOBF4iqNz9i4JP5Gjn7slm1g54wt2P3cHpRQCO6/bwLl6lXTdh4u1Zyz3b31eMkQTGTbkna7lbj0eKMZLAxPG3FXcIIiIlVYl4KnKPr24o8kRp/LFPF8u5ldmkr5RT0heDkj4RkT1GiUj6jvvyxiJPlCZ0fapYzq2s3tMnIiIiUqaU1Xv6RERERHaoNL17V5U+ERERkTJAlT4RERGRGCKlqNKnpE9EREQkhuJ8g0ZR0/CuiIiISBmgSp+IiIhIDJrIISIiIiJ7FFX6RERERGLQRA4RERGRMkDDuyIiIiKyR9G7d0sn/agiIrKnKxEltiPG3V7kf6f+0PNhvXtXRERERHYP3dMnIiIiEkNpuqdPSV8p1uugO4s7BD6b8e+s5V4H31WMkQQ+m/ZA1vJx3R4uxkgCEybenrXcq+2gYoxku8+mP1jcIYiIlHlm1gsYDCQAL7v7I3m0ORZ4BigPJLv7Mfn1qeFdERERkRgikaL/7IiZJQDPAicArYHzzax1jjZ7A88Bp7r7gcDZO+pXlT4RERGRGIrp3bsdgPnu/geAmb0NnAbMimpzAfChu/8F4O6rdtSpkj4RERGRf5CZ9QH6RK0a6u5Do743BBZHfV8CdMzRTQugvJl9BSQCg9399fyOq6RPREREJIbd8UaOMMEbmk+TvA6ac2C4HHA4cBywFzDZzH5w97mxOlXSJyIiIlKyLAH2jfreCFiWR5tkd98EbDKzScDBgJI+ERERkcIqpke2TAGam1lTYClwHsE9fNE+Bv5rZuWACgTDv0/n16lm74qIiIjEUByzd909DRgAjANmA++6+0wz62tmfcM2s4HPgOnATwSPdfk9v35V6RMREREpYdx9DDAmx7oXcnx/HHi8oH0q6RMRERGJYXdM5CguGt4VERERKQNU6RMRERGJoTRV+pT0lWHX3HYS7Y8ytqZu48lBHzB/ds7Z4FCvYQ1uf+xcEqvvxfzZy3j89vdJS0un60kHc87lRwOwZfNWhjzwCX/OXVH4GG49kfZdWgQx3PUh8+cszyOGvbn90XNIrFaZ+XOW8fgdH5CWlk6jJrW56f4zaNaqAcOHfMEHr38HQPkK5XjitSsoX74cCeXi+Wb8TEY8P7FQcbVvvz/9B3QnPj6eMWOm8vZbP+Rq039ADzp2bMbW1G089tho5s1bCUCVKhW5+eYTadK0DpFIhCceH8OsWUsLfW0Arrn1JNoflXl9PmD+7LyuTw1uf+wcEqvtxfzZy3n8jve3X58Hzgyvz3g+GP5d1j7Dx97E5s1byUiPkJ6ewbXnP79T8YmIlHbFNHt3t9DwbhnV/qgWNNivNpef9BSD7/uIAYNOzbPdFTf0ZOQb33HFyU+zcUMqPc88HIAVS9byf5e9xDVnDeHNF7/iuntOL3wMXZrToHEtLj/lGQbf/zEDBp2SdwzX9WTkiMlcceozbNywhZ5nHAZAyoYtPP/omGzJDMC2v9O49crX6HfOs/Q751naHXkALQ9qVOC44uPjuPa647n9tne5/LKhdOvWmv32q5WtTYeOzWjUsAaXXPwCTz01luuu75W1bcCAHkyZ8geX9R5Kn6teYdGi5AIfO1r7Li1osF8tLj/5aQbfn89vdP3xjHzje644Jbw+4W+UsmELzz/yKR8M/zbP/W694lX6n/OsEj4RkTKi1CR9ZjbMzP4VLh9lZjPNbKqZ7fUPxtDEzPKdLl0Ex/jKzNrtaj+durZiwie/ATBn+mKqJlaiZu3EXO0O7rA/34yfCcAXn/xK527B+55nT/uLjRtSw/3/ona96jsXw6ipQR8zllA1cS9q1q6aRwxNo2KYSudurQBYv2YTc2cuJT0tPdc+qVv+BqBcuQTKlUvI9Rjz/LRs2YClS9eyfPk60tIy+HLibDp3bpGtzZGdm/P5+OCnnj17GVWrVqRmzSpUrlyBg9ruy5gx0wBIS8tg06athTj6dtmuz/Ql4W+U1/WJ/o1+o3PXnNcnY6eOLyIixfPIlt2ltA7vXgg84e6vFXcgJVWtutVIWrE+63vSyg3UqluNNckpWeuq7V2ZTSmpZKQHSUPSiqBNTj3PaMfP38Z8AHj+MayMjmF9GMPG2DGEbXYkPj6OIW9dQ4PGNRn1zk/4jCUFjqt27aokrdqwPa7kFFq1apCjTWL2Nkkp1K6dSHp6BuvXb+aWW05i/2Z1mTd3Bc8++wWpqdsKfPxMteomxviN8rs+G6hVb8fXJwI89GJvIpEIY96bwtgPfi50fCIismcp0UmfmVUB3iV4/UgC8AAwH3gKqAokA73dfXnUPlcC5wA9zay7u18Yo+//C9tVBEa6+z1m1oTgQYffAkcA04DXgPuAusCF7v6Tmd0LNCN4IfK+wGPu/lKO/isBzwPtgDTgRnf/0sy+AQa6+9Sw3XfANcACYAhwEMHvcq+7fxxWKl8DWhM8oLFIKpdxcbnvUYjkqIfl0SRXm7btm9LzzMO56ZL8XiEYI4Y81uX8F1CeMRTgX0kZGRH6n/scVRIrcffT57PfAXVZNH9VAQPL49rkCizvuBIS4mnevD5D/jOeOXOW0b9/d847vxPDXptUsGNnCyOvOHYYau5Y83DjJUNZk5RC9ZpVePjF3ixemMzvvywsdIwiIqWdJnL8c3oBy9z9JAAzqw6MBU5z9yQzOxf4N3B55g7u/rKZdQFGu/v7eXVqZscDzYEOBH99f2JmRwN/AQcAZwN9CF6DcgHQBTgVuAPIvHmtLUFiWAX4zcw+zXGY/mE8B5lZS+BzM2sBvAz0Bq4Pv1d09+lm9hAw0d0vN7O9gZ/M7AvgamCzu7c1s7bAr4W/jIFTzutIr7PaAzD39yXUqb99SLZOvWqsWZWSrf36tZupkliJ+IR4MtIzqFM/e5umLepx/X1ncNc1w0lZv6VgMZzbgV5nBqPTc2cupU696BiqsyZpQ7b2uWLIo01+NqWkMn3KQtp1bl7gpC85KYU6UdXEOrUTWR1VXcuzTZ1EVq9OIRKBpKQNzJkTTIqZNGkO553fqcDxnnJuR3qdFXV9cv5GO7w+uX/HvKxJCtqsX7OJ7yfOxto0VNInIpKH0pT0lfR7+mYA3c3sUTM7iqCq1gYYb2ZTgUEEVcDCOj78/EaQRLUkSAIB/nT3Ge6eAcwEJrh7JIylSVQfH7v7FndPBr4kSCCjdQHeAHD3OcAioAXwHnCymZUnSFaHRcV0W3heXwGVgMbA0cCIsJ/pBK9b2Smj3v6R/mf/l/5n/5fJE2dz3KmHAtCy7b5s2rg129BupulT/uCoHgcC0P3Uw5j85WwA6tSvzl1PX8jjt7/P0kWrCx7DOz/R/9zn6H/uc0z+cjbHnXJIEMNBjdi0MTXb0OX2GP6MiuEQJn85J99jVK9RmSqJlQCoULEchx6xP4sXJhU4xjlzltGwYQ3q169OuXLxdO3Wiu8nz8vW5vvv53F8jzYAtGrVgE2btrJmzSbWrt1E0qoUGu1bE4BDD2tSqIkco975kf7nPEv/c55l8sRZ269P20ZsStlagOtzKJO/mp3vMSruVZ69KlfIWj6s0wEsLGgVVERE9lglutLn7nPN7HDgROBhYDww090LXjrJWxzwsLu/GL0yHN6Nvus+I+p7BtmvV84xtJzf8/yngbtvNrPxwGkEw8vtotqf5e6eI6a8+t5lP33jtD+6Ba+OuZGtqdt4atCHWdvuf+4SnrlnJGuSUnjl6XHc/th5XDqwBwvmLGPch8G9Xxf27Ubi3pWzZpSmp2dw7XnPFTKGubTv0oJXR98QxHB3VAz/vZhn7vsoiOGZz7n9sXO4tP9xLJiznHEjfwGgRq2q/OetvlSuUpFIRoTTL+rE1WcMoWbtRG568CwS4uOIi49j0ue/89Okgt9zmJERYciQ8Tz66HnEJ8Qxdux0Fi1M5uRTgiR59Kjf+PHHBXTs2Iw3RvQlNXUbjz+2vdA7ZMjn3HHHqZQvl8Dy5et47LGcReBCXJ+jWvDqpzeyNfVvnror6vo8ezHP3PtR1G90LpcO6B5cnw+jrs/b10Rdn85cffp/qLZ3Ze5+Jnhvd0JCPF+Onc4v383LMwYRkbKuGOddFLm4gtz/U1zMrAGwxt1Tzex0giHXFsDF7j45rJa1CF9CPIxwSDd6OUa/xxPcH3icu280s4bANqByuF+bsF10n00yt4X39J1O1PBuuFwhqs2NwIHufkU4jDs+jHVrmMiOAr5x93PDYz0EVCO43y9iZoe6+29hP63d/UozawNMBY5w9/zuvI8A9DrozkJd793hsxn/zlrudfBdxRhJ4LNpD2QtH9ft4WKMJDBh4u1Zy73aDirGSLb7bPqDxR2CiAjEKJ780+zD+4s8UfIz7y6Wcyvpw7sHEdzbNhW4E7gb+BfwqJlNI0iAOhe2U3f/HHgTmGxmM4D3gdzPK8nfT8CnwA/AA+6e88nGzwEJYf/vEEw42Roe/xdgA8EEjUwPAOWB6eFjXzKzk+eBqmY2HbglPK6IiIj8AyKRuCL/FJeSPrw7DhiXx6aj82jbO6/lfPoeDAzOY1ObGH0ujN4GzHX3Pjn6zGrj7qkEEzZyCSuY8cDnUftuIZi0kTPOLcB5+Z+NiIiISP5KeqWv1DGzS4AfgTvDySIiIiJSUkV2w6eYlOhK364ys4MIZ9BG2eruHXelX3e/dxf2fR14fVeOLyIiIv+M0vTIllKd9Ln7DOCQ4o5DREREpLiV6qRPREREZFeU4IecFJru6RMREREpA1TpExEREYlB9/SJiIiIlAWlKOnT8K6IiIhIGaBKn4iIiEgMmsghIiIiInsUVfpEREREYilFlb64SGmqW0om/agiIrKnKxEzKJr+7+Ei/zv1zwtvL5Zz0/CuiIiISBmg4V0RERGRWErR2JmSvlKs+9H/Lu4Q+GLSnVnLPTo/WIyRBMZ/Pyhr+ajTHy/GSALffPR/Wcs997q4GCPZbtyWN7KWj7joqWKMJPDDiBuLOwQRkVJBSZ+IiIhIDKXpjRy6p09ERESkDFClT0RERCQW3dMnIiIiUhZoeFdERERE9iCq9ImIiIjEUoqGd1XpExERESkDVOkTERERiaUUVfqU9ImIiIjEouf0iYiIiMieRJU+ERERkRgiGt6VPVH7DvvT79rjiY+PY+ynU3n7f5Nztel/7fF0OKIZW7du47GHRzN/7oqsbfHxcTw39HKSk1MYdNu7APS5phtHdG5OWlo6y5au4/FHRrFp49YCxdOu4/70u74n8QlxjB01lXfe+D5Xm343HE+HTgewNXUbjz84ivlzV1C+QgJPPXcJ5cuXIyEhnm++nM3rr0wC4Oiurbj4iqNp3KQ2A698lblzlu/MpaLDoU247srjiI+PY/T46fzvw5+ybW/csCa3DzyBFs3q8tKIb3n74ylZ224b0IvO7fZn7frNXHrdsJ06fizXPHkxHXoeTOrmrTzZZyjzpy7K1ebUvt05Y0AvGjSrx9mNrmHD6o3Ztrc4vCnPfH0vD138X74dOSXX/gV1RNsm3HDxscTHx/PJVzN4Y1T2vvbbpwaD+vTEmtTlhfe+480xvwDQeJ8aPDjgpKx2DetWZ+j73/POuN92OhYREdmxPWJ418x6m9l/w+W+ZnbJbui/wQ7avGxmrYvyuP+k+Pg4Bt7Qizv+722uuORFuh53II33q52tTYcjmtGwUU0uveB5nn58DNfd2Cvb9jP+1Z6/FiVnW/fLz39yZe+h9LnsZZYsWc35F3UueDw3n8AdN73FlRe8QNfuB9K4SY54OgXx9D7nOZ55dAzX/t8JAGz7O53/GziCvpe+RN9LX6LdEc1odWBDABb+sYr77niPGVP/KtT1yRnbjVf34Ob73+figa/S/ahWNGlUK1ubDRtTGfzyBN7+KHfSNHbi79x8//s7ffxY2vc8mIbN6nFZm5sZPOBVBv7nsjzbzZw8j9tOfIQVi5JybYuPj+OKB8/jl/EzdimW+Lg4br60Gzc8NpLzbxnG8Ue0pEmDmtnabNiUylNvfJmV7GX6a/laLrlzBJfcOYLeg/5H6tY0vv55/i7FIyKy20R2w6eY7BFJXzR3f8HdXy/ibnsD+SZ97n6lu88q4uP+Y6xVA5YtXcPy5etIS8vgqwmzOLJLi2xtOndpwfhx0wGYPWsZVatWomatqgDUrpNIx04HMObTqdn2+WXKn2SkB3+CZ89cRp061QoWT+sGLFuyhhXLwni+mEnno7LH0+ko44vPZoR9L80WT+qWbQCUKxdPuXLxRML6+1+LVrPkrzUFvi55adV8H5YuX8vyletJS8tgwrdz6NLxgGxt1q3fzJz5K0hLz8i1/7RZS9iwMXWXYshLp5MP44s3vwVgzk8LqFK9MjXrV8/VbsG0Raz8KznXeoDT+h3Ptx9NYV3Shl2KpXWz+ixZuY5lSetJS89g/A9zOPrwZtnarN2whdl/rMzzGmVqd2Bjlq5ax4rVKbsUj4jIbhOJK/pPMdntw7tm9hGwL1AJGOzuQ81sI/Ai0BVYC5zn7klm9hUwFegAVAMud/efcvR3L7DR3Z8wswOAF4A6QDpwNrAS+BioAZQHBrn7x2bWBBgLfAt0BpYCpwEnAe2A/5nZFqCTu2/J4zy+Am5295/D+AcDJwNbgNPcfaWZ1Qvj2T/c7Rp3/97MbgQuD9e97O7PhPF8FsZzBDANeA24D6gLXOjuP5lZFWAIcBDB73Wvu39c4B8gVLt2IqtWbf+LNSlpAy1bN8zVJmnVhmxtatdOZM3qjfQb2IOXnp9I5coVYh6j14kH89XEguXFteskkrRy+7GSk1Jo2bpBrjarsrXZQO06QTzx8XE89+oVNGhUk08+/Jk5s5YV6LgFUadmVVYlR12r1Sm0ar5PkfW/s2o3qEHSku0JbfLSNdRqUJM1K9YXaP9aDWrQ+dR23NrrIVocvv+Od8hHnRpVWbVm+zVatWYjBzYr/DXq0cn4fLLvUiwiIlIw/0Sl73J3P5wgsbrWzGoBVYBf3f0w4Gvgnqj2Vdy9M9APeHUHff8PeNbdDyZI5JYDqcAZYd9dgSfNLDOtbh62PxBYB5zl7u8DPxMkWYfklfDloQrwQ3jcScBV4fr/AF+H6w8DZprZ4cBlQEeC5O4qMzs0bH8AQfLYFmgJXAB0AW4G7gjb3AlMdPf24fk8HiaChRKX1z8sctydGpdHo0gkQsdOB7Bu7WbmRd3fl9MFFx9JenoGE8b/XrB48niXYc6bZfOKObOil5ERoW/vlzn/9MFYqwY02b9OgY5bwOBKphi/T0H1ffwiXhn0NhkZuz62kOefp0KOWZRLiOeow5ox8ce5uxyPiMjuEhcp+k9x+ScmclxrZmeEy/sSJF4ZwDvhuhHAh1Ht3wJw90lmVs3M9s6rUzNLBBq6+8iwfWq4vjzwkJkdHR6nIVAv3O1Pd88cn/wFaLKT5/Q3MDqqnx7hcjfgkjCedGC9mXUBRrr7pjC+D4GjgE/CeGaE62cCE9w9YmYzomI7HjjVzG4Ov1cCGgOzCxNwUlIKdesmZn2vU6caq5M35mizgTp1q2Vvs3ojRx/bkk5HNqfDEc2oUKEclatU5LZBp/LIg58A0KPXQRzR6QD+74b/FSKeDdSpt/1Ytesksjo5+xBf0qoU6tarxsysNrlj3rRxK9N+W0S7js1Y+Efue9h2RtLqjdStHXWtaiWSvGZjPnvsPqdc3Z0TLjsWgLm//EGdRtvvm6vdsCZrlq8tcF8tDmvK7a/3B6B6rUQ69DyY9LQMJo/6ZQd75rZqzUbq1tx+jerWrErS2sJdo04HN8UXrmTNhs2FPr6IiBTebq30mdmxQHeCIdODgd8IkpacIjGW8/qeKVY95kKC4d7D3f0QguHezGNGTytNZ+eT3m3unhnXjvrJr24UHU9G1PeMqD7jCCqSh4Sfxu5eqIQPwOcso2GjmtTfpzrlysVz7HGt+f677BWWyd/Oo0fPtgC0at2ATZu2smb1Rl4Z+hXn/2sIF537LP++byRTf12YlfC177A/513Qibtuf4+tW9MKHs/szHj2DuLpfiCTv80Zz1y69zooiOfAhmzalMqa1RupvndlqlStCECFCuU4rF1TFi/K+x62nTFn3nIa7VODfeoG1+q4Li359qfimWgw6sUv6HfEIPodMYjvR/1C9wu6ANCyQzM2b9hc4KFdgEtb3cilLYPPNyOnMOT6YTuV8AHM/mMF+9bfm33qVKNcQjw9jmjJN7/+Uag+jtfQrojsCUrRRI7dXemrDqx1981m1pJgeBOCZPNfwNsEQ5rfRu1zLvBlWCFb7+7rzSxXx+6+wcyWmNnp7v6RmVUEEsJjrnL3bWbWFdivAHGmAIk7bLVjE4BrgGfMLIFgGHgSMMzMHiFI4M4ALi5En+OAgWY2MKwCHuruhX62RUZ6hCHPjOORJ84nPj6ez8ZMY9HCZE4+9TAARn/yKz/+MJ8OnZrx+lv92Lp1G48/PHoHvcKA63tSvkI5Hn3qAgBmz1rK4CfHFiie/z71GQ8/fT7xCfGMGz2VRX8mc/LpYTwf/cpP38+nY6cDGP5ef7ambuOJf48CoGatqtxy16nEx8cRFx/HpAmz+fH7ICk78mij/409qb53ZR584lwWzFvJ7Te8VahrlZ4R4emXvuDJe/5FfEI8n34xg4WLV3Naz4MB+HjcNGruXYWXnriYKpUrkBGJcPYph3PxwFfZvOVv7rnxZA5tsy/Vq+3FBy/35dW3v+PTL3ZttizAT59No33PQ3ht5hNs3fw3T179Uta2B0bezNP9XmbN8nWc1u94zr7xJGrWq84LUx7ip8+m8Uy/V3b5+NHSMyI8MfxLBt9yVvBYm69/58+lqzmjW/CPhpETp/P/7N13eBTFH8fxdy6hppBOb1KG3kMTQTooUkQQRZAiChJBBBRQEFGx0JsoIoIg/lSQLr1JlSYllMFEkJoOKYQAKb8/9rj05AjBg/B9PQ8Pe7uzu5+b21wmM1vcCxVk4cc9cSyQl4SERHq0q0OP9xYRc/M2+fI6UL9aaT5fsCVHcwkhhMjYg270bQAGKqWOAxrYb55/A6iqlDoMRGA09O66ppTai/lCjiy23wv4Rik1AbiDcSHHj8AapdQhjItCzliRcyHwdWYXclhpKDBPKdUfowdwkNZ6n1JqIXD3gpT5Wuu/zBdyWONjYDpw3Hxu4nmMC0ju2YH9ARzYH5Bi3trVR1K8njVtY6bbOHb0AseS3Q7l1ZfnZieKkWdfAAf2pVx/7cpUeaZsSLPeuYBgBvWZn+429/yh2fPH/fce7T98jv2HUzaUVm08ZpkOv36Drq99ne66H03NurGcXXOGLUp3/tguky3Tq77axKqvNmW6nSmvz7vvLPuOnWPfsXMp5q3YdtwyHR4RQ8ch36ZeDYBbt+NoOyj7x44QQvxnctFj2Ozu5UTwnKKUitZaO6UzfwfmK2T/81C5SyJAq6af2joHW/543zLduvEnNkxi2Lz3A8v0U50n2TCJYdfKkZbptgXupQP4wdl4c7FluuErU22YxLB/yTu2jiCEsI2HorVV5uvJOd5QOj9whE3e2yN3nz4hhBBCCHHvbPIYtvR6+czzn/6Po6ShlFoBlE01+z2tdebjnkIIIYTIfeTZu7mX1rpL1qWEEEIIIR4t0ugTQgghhMiI9PQJIYQQQjwGctHVu3IhhxBCCCHEY0B6+oQQQgghMmDLZ+XmNOnpE0IIIYR4DEhPnxBCCCFERqSnTwghhBBCPEqk0SeEEEII8RiwybN3xQMnH6oQQohH3UNxr5QnZk7J8d+p/wwZLs/eFUIIIYQQD4ZcyCGEEEIIkZFcdHNmafTlYg17TrF1BPb/ONwy3bTTJBsmMfyxaqRlul3V922YxLDh5KeW6fYlh9owSZL1F2dYph+6OvIaaMMkhvUhX9s6ghBCZIs0+oQQQgghMmKjs+SVUu2AGYA9MF9r/XkG5XyA/cCLWutlmW1TzukTQgghhMhI4gP4lwWllD0wB2gPVAFeUkpVyaDcF8BGa96KNPqEEEIIIR4u9QF/rfU/WuvbwP+ATumUewtYDgRbs1Fp9AkhhBBCZMAuMef/WaE4cDHZ60vmeRZKqeJAF8DqE43lnD4hhBBCiP+QUup14PVks+Zprecle53eJcOpm4vTeNSgMAAAIABJREFUgfe01vFKKav2K40+IYQQQoiMPIALOcwNvHmZFLkElEz2ugRwJVWZesD/zA0+T+AZpVSc1nplRhuVRp8QQgghREZsc/XuQaCCUqoscBnoAbycvIDWuuzdaaXUQmBtZg0+kHP6hBBCCCEeKlrrOMAX46rc08AvWuuTSqmBSqls37BUevqEEEIIITJg5YUXOU5r/Tvwe6p56V60obXuY802padPCCGEEOIxID19QgghhBAZkWfvikddwxplGNarOSaTHat3+LF4zYEUy0sXdeeDN9qiynjz9S97WPr7Icsyp4L5GDOgDU+U8ITERD6ZtxE//6v3lad+7TIMGdASk8mOdZuP8+PylHlKFXdn1JD2VCznzfwlu/nfyoOWZe+91Y7G9Z7gWkQMfYYsvK8cg0Y/i09Txa2bd5jy/nL8T6e+WAoKF3dj9OQXcS5UAP9TV5g0ehlxd+Jp/mxNuvdvCsDNmFvM+ng153QgefI6MPmHAeTJa4+9vYldm06yZM5Wq/IM/Oh5fFpUMfK88yMBfpfS5inpzqg5r+Ls6oi/30UmD11C3J14ur7RguZd6gJg72BPyfKF6VHrfaKvx7Bw7zhibtwiIT6B+PgEhj5r/XOaH0QdASzaNMLIlJBIfFwCQ178yupM6Rk4sTs+rapxK+Y2U4YsIuD4xTRlnuv/NJ3faEGxst68qIYTGX7jvvYphMiFbDS8+yBIo+8xZLKzY0Sflgz5bBnB4VF8/3FPdh3x5/zlcEuZyBs3mfrDNprVLZ9m/WG9mrP/2HnGzFiDg72J/Pny3F8ekx3D3mjNOx/+QkhYFPMm92L3gQD+vRiWlCc6lpnfbqVJw7R5Nmz1Y8W6I4x5+5n7yuHzVEWKlfakX/upVKpREt9xHXn7pbSnT/R/py0rftjDzvUneGtcJ9o+X5d1Px8g8PI1Rvb5lujIWOo1qcjQ8Z15+6WvuXM7jvf6fUdszG3sHUxMWfw6h3ad5Uw6jZAUeZpXoVhZL/o/9QmVapfGd2I3hnWclqZcv9EdWTl/BztX/4XvxO607dGQdYv3sPybbSz/ZhsADVpVpfNrTxN9Pcay3qjus4m8dm+NnAdVR3e91/c7IpNlzC6fVtUo9oQ3/euPo1Ldsvh++TLD2n2RptypAwH8uekEX6585773KYQQD7tH4pw+pVQZpZSfebqeUmpmDm//aaVU4yzKDFRK9c7J/dpKlXJFuBR0nSshEcTFJ7B5v6ZpqsbdtcibnP4niLj4hBTzCxbIS+1KJVi94wQAcfEJRMfcuq88lSsU5XLgNa4GRRAXl8DWXWdoUj9lnusRMZzxDyQ+LiHN+sdOXSIyOva+MgA0alGZrav/AuDM8Ys4OefH3dM5TbmaDZ5g16aTAGxZdYTGLY3HIZ4+eoHoyFjz+hfwLFzIsk5szG0AHBzscXCwJzEx6z8dG7apxtblRo/mmb/+xcmlAG7eLmnzPFmBXeuOGXmWHaBR2+ppyjTrVJedq45kuc+sPMg6ykkN29Vg68/7jf0cPodToQK4FU5bdwEnLhKc7I8LIYRIzUZP5HggHrmePq31IeBQlgXvzdNANLA3k/1a/ZiTh52XuxPBYVGW18HhUVQtV9SqdYt7F+JaVAxj32hL+VLe6HNBTF28jdhbcdnO4+nhRHBoUp6QsCiqVLQuT07y8HYhJDAiKUdQJB6FXQhPls3FtSA3omJJMDeGQ4Ii8UinIdb2+Xoc2nXW8tpksmPWr4MpVsqdNT/9iT6Rdpg2TZ4iroReuW55HXo1As8ihbgWHJmUx82RG5E3LXlCr17Ho4hriu3ky5+Hek9X4quxyyzzEhPh0x8HkZgI63/cw/ql+7LMAw+2jhITE5n4bV8SExP5/deDrP/1YJp1rOVR1JXQK9csr0OvXMeziCvXgiIzWUsIIXI3qxp95h6uERgj28eBD4AFgBcQAvTVWl9IZ71xwHNAAYwG1Rta60Sl1A7gKMYDhV2AflrrA0qp8UA5jOfLlQS+1Fp/m2qbTwMjtNYdlFJOwCyMu1InAh9prZcrpeYCPub9LtNaf2he9zywyJwpD9ANiAUGAvFKqVeAt7TWu9J5L+OBaK31ZHP+P4HmgCvQX2u9SyllD3wBtDXn+VZrPUsp1RKYbK7vg8AgrfUtc56l5u3kwXgky2dAeWDS3YamUmok0B3IB6y4+36yyy69p7tY+ZeHvcmEKlOYqYu2cTIgkGG9mtP7ufrMW5Zhe9mKPOnEscFfQnZ2aZOk7pFLp0iaMjXql6Xt83UZ3ivpZusJCYkM7jobR+f8jJvZk9LlvfnXP/PnY1uzL2vKNGhdjVMHz6UY2h3+/HTCgyIp5OHExKVvcjEgGL8/AzLNY+zvwdXRO6/MIzwkikLujnw2vy8X/wnB7/D5LDNZnzNbmxJCPO5y0XdHlsO7SqmqwPtAC611TWAoMBv4QWtdA/gRyGi4dbbW2kdrXQ2jAdYh2TJHrXVj4E2MBuRdNYBngUbAOKVUsUzijQUitNbVzVm2mee/r7WuZ95WM6VUjWTrhGqt6wBzMRqP5zEeVjxNa10rvQZfBhy01vWBt4G7jbDXgbJA7bt1o5TKDywEXtRaV8do+A1Ktp2LWutGwC5zuReAhsAEAKVUG6ACRgO5FlBXKdXUyozpCg6PwtsjaUjO292ZkOvRVq8bEh7FyQDj5PttB86iyhS+nziEhEXjnWyI0MvDmdBw6/Lcr+deasCc5b7MWe5LWEgkXkWShhu9CrsQHhyVonzEtRgcnfNjsjcllQlJKlO2YmHe/qgLH721hKiIm2n2dyMqluMHzlGvScV083R4tQmzN4xk9oaRhAVF4FksqdfOs2ghwlL1VEWE38DRpYAlj2dRV8KDIlKUadaxDjtWpxzaDTdvJyIsmr0bjqNqlUq/gvjv6uhumYjwG+zdcgpVvUSGmdLToV8zZm9/n9nb3ycsMALPYm6WZZ7FXAkLup7J2kIIkftZc05fC4zeslAArXU4RoNsqXn5YqBJBus2V0r9qZQ6Yd5O1WTLfjJv7w/ARSl197fbKq31TfP+tmM0djLSCphz94XW+u54Tnel1BHgL/M+qyRb5zfz/4eBMplsOyvpbacV8LX5Ttp360oB57TWd8exFgHJG22rzf+fAP7UWkdprUOAWHOdtDH/+ws4AlTCaARm2+l/AilZxJWiXi442Jto3VCx63DWvTwA4RExBIVFUaqo8QvVp2opzl2+v3Oizvx9lRJF3SjqXQgHBxMtn6rEngP+97VNa6356U8Gd53N4K6z2bf1NC071gagUo2S3Ii+lWLY8q7jB/7hqTbGodyqUx32bTsNgFfRQoyd0ZNJo5dx+d+kOinkVhBH5/wA5M3nQO1G5bh4LiTdPGsX7ca33SR8201i38YTtOzqY+SpXZobUbEphnYtefb+zVPP1jTyvFCffZv8LMsKOuenesNy7Nt4wjIvX4G8FHDMZ5mu07QS53XGV1//F3WUr0AeChTMa5mu07g85/2DMsyUnrULduLb/FN8m3/KvvVHafliQyNn3bLciIyVoV0hRLY8buf02ZF152YigFJqI1AY45w7X+AroJ7W+qJ5eDR/6nXSeZ3RfKuymZ9TNwLw0VpfMz+PLvl+7151EM/9ndOY3nbSq6usbvBzdzsJyabvvnYwr/+Z1vqb7EdNKT4hkckLtzHjva6YTCbW7vTj3OUwurQ0OkRXbD2Oe6GCLPzkFRwL5CUhIZEe7evQ492FxNy8zZQftvHRm8+Qx8Gey8ERfPLNhvvOM33eFiaPfwGTycTvW09w/mIYHdsZDZnVG47h7urIvCm9cCxo5Hnhubr09l1AzM3bjBvegdrVSlLIpQDLvhvI9z/tYd2WE1nsNa0Df2h8mlZkwfp3uBV7h6kf/GZZNmFub6aPW0F4SBTfTd3I6Mk9eHVIawJOX2HjcuMU054DW+BcqCC+Yzsa78t82xF3L2eGT3wBe5MJO5Mdf2w8wYGdOss8B7edwqdFFRbsHkvszdtMG77UsmzCojeY/u5PhAdFsuCzNYya8yq9Rz5LgN8lNv0v6fy8xu1qcOQPza2bty3z3LycGfttfwDs7U3sWHWYwzvO2LSO3DycGDezpyXT9nXHObz7b6sypefgZj98WlVjwYGPjbobsigp50++TH97MeFBEXQc0Jxuvm1w83bhq51jObjFjxnDlmR7v0KIXCgXDe/aZXUVoXl4dwXQSGsdppRyxxiG/FVrvVgp1QfopLXukmo9V0Bj9ILZA/sxegzHm8+JO6O1HqiUagLM1VpXNzcMO2MMbzpi9G41BPJiPEi4Wqpz+j4H8mut3zbv0w0oBfwA1MY45/A48J7WeqH5HLp6WutQpVQ9YLLW+mml1HDAJbNz5dI5p2+E1vqQUsoTOKS1LmN+Hl4roIfWOs5cVzHAWYzhcX9zI/QvrfWMVHn6mKd9zfs7j3GuYh3gY6Cl1jpaKVUcuKO1zuyEsESAhj2tv/fag7L/x+GW6aadJtkwieGPVSMt0+2qvm/DJIYNJz+1TLcvOdSGSZKsvzjDMv3Q1ZFXth85mWPWh+Saa7qEeNg9FHdFrvjptBxv9p19f5hN3luWw7ta65PAp8BOpdQxYCowBOirlDoO9MI4zy/1eteBbzGGLVdiXMCQ3DWl1F6M8+n6J5t/AFiH0Uj8WGud9s6vST4B3JRSfuZszbXWxzAaiycxzhXck9V7BNYAXZRSR5VST1lRPiPzgQvAcXOel7XWsUBf4FfzMHcCxnu2itZ6E8ZQ+j7z+suAtPfIEEIIIUTOS3wA/2wky56+ByF5T1mq+eMx96b956FyF+npy4D09GVNevoyJz19QvxnHo6evk8eQE/fB7bp6Xvk7tMnhBBCCPFfseWFFznNJo0+rfXTGcwf/98mSUsp9T7G/fuS+1Vr/Wl65YUQQgghHgXS05eKuXEnDTwhhBBC5CrS6BNCCCGEyEguGt615ubMQgghhBDiESc9fUIIIYQQGZALOYQQQgghHge5qNEnw7tCCCGEEI8B6ekTQgghhMiI9PQJIYQQQohHiU0ewyYeOPlQhRBCPOoeisewVR6X849hOz3BNo9hk54+IYQQQojHgJzTJ4QQQgiRkVw0diaNvlysaadJto7AH6tGWqYbvTzFhkkM+5YOt0w/2c32efb8mpSntSn1I59tY3PCr5bpJl0n2zCJYffyEZbpsks+s2ESw7lXRlumffpNtWESw8EF79g6ghC5Wm66T58M7wohhBBCPAakp08IIYQQIiPS0yeEEEIIIR4l0tMnhBBCCJGRXNTTJ40+IYQQQogMyIUcQgghhBDikSI9fUIIIYQQGZGePiGEEEII8SiRnj4hhBBCiIzkop4+afQJIYQQQmRALuQQQgghhBCPFOnpe0zVr12GIQNaYjLZsW7zcX5cfiDF8lLF3Rk1pD0Vy3kzf8lu/rfyoGXZe2+1o3G9J7gWEUOfIQtzJE/DGmV4u3dz7E12rN7ux+I1KfOULubO+2+0RZXx5ptf9rB03SHLMqeC+Rg9oA3lSnqSmJjIp/M24vf31fvK06BWGd7u2xyTyY41W/1YsjJV/RRz5/3BbalY1pt5P+3hpzWHUiw3mez47vNXCAmP4t3PV95XluTenNGX+u3rcCvmFpP6zsH/r3NpynQa3I4uQ5+lePkidPXqR2RYlGVZjWZVeHNaX+zz2BMZGsXw5h9mO0uDWmUY2q8FJpMda7eeYMmKtMfQmMHtqPiEN98u3c1Pq406ypvHntkf9yBvHnvs7U1s33eWBT/vzXaOu5oWfYIPfVphsjPxs/9Rvj65P2XewqWY16wrl6IjANhwUTPrxB4Avmj4DC1KlCcsNoZ2a+ffdxaARtXKMPzlpzHZmVi16wSLfj+YYnnpIm6M69eWSqW9mfvbHpZsPGxZ1qNVbTo3rY6dHaz84wQ/bf4rRzIJIbIhF/X0SaPvMWQy2THsjda88+EvhIRFMW9yL3YfCODfi2GWMpHRscz8ditNGpZPs/6GrX6sWHeEMW8/kzN57OwY3rclQz9bRnBYFAs+6cmuI/6cvxyeLM9Npi3aRtN6afMM692c/cfO8/6MNTjYm8ifL8/95THZMbx/S97+eBnB4VHM/6wnuw/5c/5SqjwLttG0fto8AN2eqcP5y2E4Fsh7X1mSq9++NsXLF6VPxbeo3KACQ74awJBGY9KU89tzhv1rDzN5+/gU8x0LFWTInAGMbv8pIRdDcfVyyXYWk8mOdwa0YtiEXwkOi2L+F6+w+2AA5y8lO4aiYpn+3TaaNkhZR7fvxDN0/C/cjL2Dvb2JuZ+8xJ9HznHyPhrqJjs7JtRvQ6+t/yMwJpJV7fuw5dLf+EeEpSh3MPgSr+34Nc36y/85wQ9nDzOl8XPZzpA6z7uvtMB3ynKCwqNYNK4nfxwN4NyVZMfQjVimLN1Oszop66dccQ86N63Oq58sJS4unpnvPM/uY+e4GHw9R7IJIR5fj+zwrlIq2tYZ/ktKqTJKqZdzYluVKxTlcuA1rgZFEBeXwNZdZ2iSqvFyPSKGM/6BxMclpFn/2KlLREbH5kQUAKqUL8KloOtcCY4gLj6BLfs0TeumzHMt8ian/wkiLj5lnoIF8lKrUgnW7DgBQFx8AtExt+4rT+XyRbgUaM4Tl8DWPZqnUjU2r0fe5ExAEHHp1I+XuxON65RlzdYT95UjtUadfNiyeCcAp//8GydXR9yLuKYpF3D0PEH/hqSZ3+LlJuxe8SchF0ON9xASme0sRh1d44r5GNqy+wxNfMqlKHM9MoYzAYHp1tHN2DsAONibsHcwkXiff0rX9CjGv1HXuBh9nTsJCaw5f5rWJSpavf6B4Itcv5Vzx3TVJ4pwMfg6l0OMY3rzn2doVitl/VyLusmp82mP6TJF3Tnxz1Vu3Y4jPiGRI/oST9dJ/48LIcSDZ5eY8/9s5ZFt9D2GygA50ujz9HAiODRpyC8kLAovD6ec2HS2eLk5EZxsCDI4PAovd+vyFPcuxPWoGD54oy2LJvZi9IA25M93fx3YXu7p5LmH+hnatzlfLfmDxISc/cn2LOZOcLLe2NBLYXgWd7d6/RIVi+Hs5sjkbeOZc/ALWvVqmu0sXu7OKY+h8Gi8PJytXt9ksuP7yb1Zs+BNDh37l1N/B2Y7C0CRgk5cjUlqxAbGRFGkYNo8dbyK8/uz/fi+eXcqFPK8r31mxsvViaDwpPoJuhaNl5t19RNwOYzaFUtQyDE/+fI60Lh6WQq7W1+3QgiRkUd+eFcpZQd8CbTHGHn/RGv9s1LKCVgFuAF5gA+01quUUmWA9cBuoDFwGeiktb6ZwfYHAK8DeQF/oJfWOkYptRC4CVQCSgN9gVeBRsCfWus+5vVfAsYAdsA6rfV75vnRWmsn8/QLQAetdR/zdiOBekAR4F2t9TLgc6CyUuoosEhrPS27dWaXzrxEG/7lYWeXNpG1eexNJiqWKcyUhds4FRDI272b07tjfeb9mv1zxOzSqSFr8zSuY5zrqP8JpnaVEtnOkG6u+6gnAHsHeyrUeYJ3W00gb4G8zNz7Kaf3/83lbAyrphOFxHsIk5CQSN8RP+BUMB8T3+tE2ZKenDP3QGZH+p9ZyjwnwwNpsmIOMXF3eLpYOb5p1pUWq7/J9j4zzXMf9XP+ajg/rD/I7BFdiYm9w98XQ4hPSNtbKoT4j+Sic/pyQ0/f80AtoCbQCpiklCoKxAJdtNZ1gObAFHMDEaACMEdrXRW4DnTNZPu/aa19tNY1gdNA/2TL3IAWwDBgDTANqApUV0rVUkoVA74wl6kF+CilOlvxnooCTYAOGI09gFHALq11rftp8AGEhEXj7ZnUc+Dl4UxouO1Gy4PDo/BO1kvk7e5M6DXr8gSHRxESHsWpAKOnaPufZ6lYpnDO57GyfmpUKkaTeuVYNuc1PhrWgbrVSjHurfbZztLxzbZ8fWQSXx+ZRNjVcLxLeliWeZbwICzZOWJZCbkUxsGNR4mNuUVkWBTHd52mXM3S2coVHBaV8hhyd8rWMRQdc4u//C7SsHaZbOW462pMFEULJp2jWKSgM0E3U+aJvnObmDhjWHnHlQDymEy45StwX/vNSPC16BS9c4XdnAi9bn39rN7lR6+PfuSNL34h8kYsF4PkfD4hbCbxAfyzkdzQ6GsC/KS1jtdaBwE7AR+MDq2JSqnjwBagOHC3NXBOa33UPH0YY+g0I9WUUruUUieAnhiNurvWaK0TgRNAkNb6hNY6AThp3qYPsENrHaK1jgN+BKwZU1uptU7QWp9KljnHnPn7KiWKulHUuxAODiZaPlWJPQf8c3o3VjsdEEjJIq4U9XLBwd5Eq0aKXYcDrFo3PCKGoLAoShV1A6BetVKcvxyWxVqZO+MfSImirhT1djHq50nF7kPW5fl66W66DJzHC4Pn8+G0tRz2u8CEWeuznWX1VxsZWGckA+uMZM/Kg7Tq1QyAyg0qcCMihvBA6xsD+1YdpHqTypjsTeQrkJdK9ctz4fTlbOU64x9IyWTHUKsmldhjZR25uhTAqWA+APLmdaBejdL8e9n6xmt6joddoYyzGyUcC5HHZOK5MpXZcunvFGU88ztapmt6FMXOzo5rt9Lt4L9vp84FUqqwK8U8jWO6dYNK/HH0H6vXd3M2GqOF3Z1pXrcCG/8880ByCiEeL4/88C7pj1aC0UDzAupqre8opc4D+c3Lkp/pHw9k9uf+QqCz1vqYUqoP8HSyZXe3k5BqmwkYdRuXyXaTt/Xzp1qWfFsZvb9si09IZPq8LUwe/wImk4nft57g/MUwOrarCcDqDcdwd3Vk3pReOBbMS0JCIi88V5fevguIuXmbccM7ULtaSQq5FGDZdwP5/qc9rNuS/YsW4hMSmbJwG9NHdcVkMrF2hx/nLofRpWUNAFZsPY57oYJ8/8krOBbIS0JiIi+2q8NL7y4k5uZtpi7axvjBz5DHwZ7LwRF8+s2G+66fad9tY+r7XbE3mVi73Y9zl8Lo3NrIs3LzcdxdC/Ld50l5uj9bh57DjDwPyoHfj9Dgmdos+nsWt2JuM7nfHMuyT9eOZuqArwm7eo3Ob7Wn+8hOuBdxZd6xyRxY/xdTB3zNhTOXObjxKPOOTSEhIYH1323l/MmL2coSn5DI1PlbmTrW+MzWbTvBuYthdGpjHEOrNh3D3bUg87/sZamjbh3q8srQ7/Fwc+R93/aY7E2Y7OzYtlez97D1DaJ08yQm8uHBzfzQsgcmOzt+DTjO3xGhvFyhNgBL//6LZ0pVomfF2sQnJhAbF8eQXass689o0omGhUvhlq8Ae7sMZvrxXfwScDz7eRIS+XLJdma+09W4DdFuP/65EsbzTxvH0G87juPhUpBF43riWCAviYmJ9Ghdhxc/WMSN2Nt8Mfg5CjkVIC4+gS+XbCXqPi9OEkJkX47/ErYhu3s5D+dhcvecOKXU88AbwDOAO3AIaAC8CJTXWr+llGoObAPKmldfq7WuZt7OCMBJaz0+g/2EAlWAa8DvwOVk596t1VovM58nmHybC4G1wB5gP1DXvP5GYJb53EJ/4DlAA78CUam3m+p91gWmaq2bWVE9iQBNO02youiD9ceqkZbpRi9PsWESw76lwy3TT3azfZ49vyblaW3qZsMkSTYnJN3SpEnXyTZMYti9fIRluuySz2yYxHDuldGWaZ9+U22YxHBwwTu2jiDEg/JQtLdqvDMtxxtKx6cOs8l7yw3DuyuA48AxjIbdu1rrQIyh1HpKqUMYvX7ZHR8ZC/wJbL7XbWitrwKjge3mfEe01ne7F0ZhNAy3AdacSX8ciFNKHVNKDbuXHEIIIYTIplx0Tt8jO7x798pX8zl1I83/ki8PxbiSNj3VkpXLtCtDaz0XmJvO/D7Jps+n2mbyZUuBpemsvwxYltl2za/vvs87QMvMsgohhBAiZ8mzd4UQQgghxCPlke3py2lKqTnAk6lmz9Baf2+LPEIIIYR4COSinj5p9JlprQfbOoMQQgghxIMijT4hhBBCiIxIT58QQgghRO4nF3IIIYQQQohHivT0CSGEEEJkRHr6hBBCCCHEo0R6+oQQQgghMiDn9AkhhBBCiEeKXWJiLmrCirvkQxVCCPGos7N1AIDab07L8d+pf301zCbvTYZ3hRBCCCEyIMO7QgghhBDikSI9fblY+yJv2joC6wO/sky3c3vNhkkMG67Nt0y3LzzIhkkM64PmWqYfhjyQKlOVMTZMYlh/aqJlum3dD22YxLDx8EeW6XY1x9owiWHDsY8t083bfGHDJEm2b3rP1hGEyDnS0yeEEEIIIR4l0tMnhBBCCJGRXNTTJ40+IYQQQogM5KYLOaTRJ4QQQgjxkFFKtQNmAPbAfK3156mW9wTunkAbDQzSWh/LbJtyTp8QQgghREYSH8C/LCil7IE5QHugCvCSUqpKqmLngGZa6xrAx8C8rLYrPX1CCCGEEA+X+oC/1vofAKXU/4BOwKm7BbTWe5OV3w+UyGqj0ugTQgghhMiAnW2eXFYcuJjs9SWgQSbl+wPrs9qoNPqEEEIIITLyANp8SqnXgdeTzZqntU4+PJveY9rSTaKUao7R6GuS1X6l0SeEEEII8R8yN/AyOwfvElAy2esSwJXUhZRSNYD5QHutdVhW+5VGnxBCCCFEBmx0y5aDQAWlVFngMtADeDl5AaVUKeA3oJfW+qw1G5Wrd4UQQgghHiJa6zjAF9gInAZ+0VqfVEoNVEoNNBcbB3gAXymljiqlDmW1Xenpe8wM/KQbPi2rcuvmHaYM/YGAExfTlClcyoNRX/fD2dUR/xMXmey7kLg78TgVKsCwab0oWsaL27fuMG3YYv49c9Wynslkx8yNowgNvM74XnPTbDcrgz5/CZ/W1bl18zZT3lyA//ELaco8N6A5XQa2ptgT3nQv9zaR4dEANO/WgO5D2wNw80Yss4Yv4ZzfpXvOkJGBn3Y319ttpgxJv96e69fzWToYAAAgAElEQVSMzq+3oFhZb16sPILI8Bs5tv+HNdPAMR3waaqMDGOWE3A6zegDhYu7MWpKD5wLFcD/1BUmj/qVuDvxNGxRmd5vtSIhMZH4uATmfb6Ok0f+zXKf9RqVZ+CI9tjb27F+5RF+Wbg7TZlBI9tT/8kKxMbeYcr4lfibj9OM1n1taBsaNq3InTvxXL10jSnjV3IjOpbm7avTrdeTlu2WrVA4y3yD3nsGnyYVuRV7hyljf7PsO2WduDL6i+44uxTE/8wVJo1ZTlxcPCXKeDJ8QhfKVS7GollbWP7DHss6wz7qTIOmiuvhNxjYdXaWOQB86pXFd1BL7E0m1m04xk8//5mmzFtvtqSBTzlib93hi8m/87d/ECVLuDPu/Y6WMkWLuPL9D7tZvsL4ndKlUx06d6xDQnwi+w8E8M38HVblEeKRZKObM2utfwd+TzXv62TTrwH39FB76el7jPi0rEqxJ7zp32g8M0f8iO8XPdIt1++Dzqz8ZhuvNR5P9PUY2r7cGIAXh7Yj4OQl3mzxKZPfWsTAj7ulWK/TgOZc+Dswe9laV6dYOW/61R3DjLd/wHfKK+mWO7Xfn9GdpxB0ITTF/MB/Qxn57JcMajKepZPWMnRa72zlSDdby6oUK+tN/4YfMnPEUny/fCn9bAcCGN1tBkEXsjytIldk8mlakWKlPejfbgozP1yJ74ed0i3Xb3g7Vi7aw2vtpxIdeZO2z9cD4Oj+AN7sMgvf52cz7YPfGDrh+Sz3aTLZMXjUs3wwZAkDXphD87bVKVXWK2WuJytQvKQHfTvPZMYna3hrdIcs1z3yZwCvd/+KQT3mcvnfMHr0fQqA7etP8ObLX/Pmy1/z5bjfCLpyPfM6aVKBYqU86PfcdGZMWIXvB8+lW67/0LasWLKP/h2nG3XSpQ4AUZE3mfvF7yxftCfNOptX/cUHg37Iso6S19VQ39aMev9X+gyYT8unq1C6lEeKMg18nqB4cXde6TuPKdM3MmxIGwAuXgpnwKCFDBi0kDcGL+LWrTvs3mOMHtWqWYonG1XgtYHf0/f17/h52QGrMwnxKLJLzPl/tpJjjT6lVHRObetRoJQqo5R6OYsytZRSz/xXmbLSsG0Ntv5i/KV/5sh5nFwK4ubtkqZczScVu9b+BcCWX/bTqF1NAEpVLMqxXRqAS/5BFC7pgaunMwCeRV2p36oaG39M+8vKGo2eqcXW/+0zsh36B6dCBXEvXChNuYATFwm6mLYBc/pAANERMcb6B//Bs5hbtnKkp2G7mmz9db+x7cPnMqy3AL9LBF8Mz7H9PuyZGraowtZVxnFy5vhFnJzz42Y+HpKr2eAJdm3yA2DLyiM0alkZgNiY25Yy+QvkIdGK2yKoqsW5cjGcwMvXiIuLZ8cmPxo9XSlFmUbNKrFl3VEjl98lHJ3y4+7plOm6R/YHkBCfAMBpv4t4Fk5bl83bVmfHRr9M8zVqXpmta8z7PnEJJ+cCuHs6pa2T+mXZtfmkUSerj9K4hVEnEeE3OHvyMvFx8WnW8TvyL1GRNzPdf3KVVFGuXLnO1cAI4uIS2LbzNE82rpCizJONK7Bps/GeTp+5gqNjPtzdHVOUqVO7NFeuXicoOBKATh1qs/Tn/dy5Y2S8fj3G6kxCCNuSnr7sK0OqkyrTUQu4p0afUuqBDbl7FHUl9Mo1y+vQq9fwLOqaooyLuyM3ImMsvwBDr17Hw1zmn5OXaPxMLQAq1i6Ndwl3PIsZy974+AW++3gFCdm8n5FHUVdCLic1TkKuXLPs91617dWEQ1sy/+V8LzyKuhJ6OfN6+689DJk8vF0IDYxIyhAUmaax5OJakBtRsUnHU1AkHska841bVmHe2mFM+PpVpn2w3Kp9hgQl32cEnl4pG5qe3s6EBEUmlQmOxMPLxap1Adp2rMPBPX+nmd+0TTW2bzxxT/lCgiLw8M68TtIrkxM8PZ0JDkmqh5CQKDw9UjZAPT2cUpQJDY3C0yNlnbRoVpmt209bXpco4UaNaiX5amYvpk9+CVWxSI5nF+KhYoMncjwoOd7AUErZAV9iPDokEfhEa/2zUsoJWAW4AXmAD7TWq5RSZTBuKLgbaIxxlUonrXW6f9IqpQZg3NsmL+CPcdVKjFJqIXATqASUBvoCrwKNgD+11n3M678EjMG4B846rfV75vnRWmsn8/QLQAetdR/zdiOBekAR4F2t9TLgc6CyUuoosEhrPS1VzrzABKCAUqoJ8BmwFpgFVMeo+/HmOugDPAvkBxyVUj8AnTGet1cNmGJ+v72AW8AzWut77r6xs0t725/UvSuZlfl11ibe+KQbs7eM5vzpKwT4XSI+LoH6ratxPTQa/+MXqZ6qJ+H+st37dmo0UbR95SmGt/8868JWSvdmSTZ+APfDkCmdj+yejieAvVtPsXfrKarVLUPvIa0Z039BNvaZdaHExESr1n2pX1Pi4xPYtv54ivmqWnFuxd7h34DgzPOlMy/1Pqx6DznAuiyZ3wrMwcFE40bl+XbBTss8e3sTzs75eHPIYiqponz4QSde7v1NzoQWQjxQD6JX6XmMHq6agCdwUCn1BxACdNFaRyqlPIH9SqnV5nUqAC9prQcopX4BugJLMtj+b1rrbwGUUp9g3JBwlnmZG9AC6AisAZ7EOMnxoFKqFhAMfAHUBa4Bm5RSnbXWK7N4T0UxbnpYCVgNLANGASO01h3SW0FrfVspNQ6op7X2NeedCGzTWvdTSrkCB5RSW8yrNAJqaK3DzY3AakBtjIagP/Ce1rq2Umoa0BuYnkVmADr0bUq7nsaJ6GeP/pti2NOzqBthyXpqACLConF0KYjJ3kRCfAKeRV0JN5eJiY5l2tuLLWUXHvyYoAthNOtcj4ZtquPTsip58jlQ0KkAI2f3YZLvwkyzPfdac9r1Ns6dOnvkPF7F3S3LvIq5ER6Y+flTqZWtWoK3Z77K2G4ziLp2fxcsdOjbjHavJKu34qnr7d6y5YSHIVOHlxrSrptxTt7ZE5fxLJLUa+dZ2IWw4KgU5SOu3cDROX/S8VTYhfDgSFLzO3yeoiXdcXEtSGQmw4WhQZF4FU6+z0KEhUalUyap58zT24Xw0Cjy5LHPdN1WHWpS/6mKjBq0KM1+n25TnR0b0u/le+7F+rQzn6d49uTlFPvwKlyI8JCU7zfiWkyKOkmvTE4ICY3C2yupHry8nAkLj86gzGXA6B0MDUsq08DnCc76B3Et2WcSEhLFH7uN8/vO6KskJCRSqFABIiKsH3oW4lFiy3PwctqDGN5tAvyktY7XWgcBOwEfjD88JyqljgNbMB4xcvdSuHNa66Pm6cMYQ6cZqaaU2qWUOgH0BKomW7ZGa50InACCtNYntNYJwEnzNn2AHVrrEPPl0D8CTa14Tyu11gla61PJMmdHG2CUuXdwB0aDrpR52eZUvXfbtdZRWusQIAKjEYv5vZWxdodrv/8D31af4dvqM/ZtOE7L7sZTXCrVKcONqJtcS+cX8PG9Z3mqQ20AWnVvyL6NRq+Ho0sBHPLYA9Cu55Oc2O9PTHQsCyeuoled9+njM5bPBy7g2B6dZYMPYM387QxuOoHBTSew7/e/aNmjkZGt3hPciLxJeFBEFltI4lXCnbE/vMmkgd9xOSDI6vUysvb7nfi2nIhvy4nsW3+Mlt0aGtnqls2w3h60hyHT2p/24/v8bHyfn82+rado2ck4TirVKMmNqFiupWqAARw/8A9PtakGQKvOddi3zRgqLFoqqZFfrnIxHPLYZ9rgA9CnrlC8pDuFi7ni4GDP022qsX/nmRRl9v9xhlbPGqchVKpWgpjoWMJDozNdt16j8nR/tQnjhy3lVuydFNuzs7PjqVZV2LEp/VMG1vx8gMEvfsXgF79i3/bTtHzOvO/qJbhh3neaOjl4jqdaG19drTrWYt/2M2nK3K8z+irFi7tRpEghHBxMtGhWmb37/FOU2bvvb9q0Nj6bypWKcePGLcKTXeHdonkVtiUb2gXYvfdv6tQqDUCJ4m7kyWMvDT4hHhEPoqcvvfECMBpoXkBdrfUdpdR5jEYPGEOWd8UDBTLZ/kKgs9b6mLlH7Olky+5uJyHVNhMw3mtcJttN3pbPn2pZ8m1l9P6sYQd01Vrr5DOVUg2A1F1TqfMnf2/Z+twObvHDp2VVFuz/iNibt1P02k348U2mv/Mj4UERLPh4BaO+6U/vUc8R4HeJTUuNZzqXrFCEEbNeJSE+gQtnA5n+zuKMdnXPDmw6gU/r6iw4MpFbN28zdfD3Sdl+Gcr0IQsJD4yg0+steWFIW9wLF2Lu7vEc3HyC6UMX0XPkczi7O+I7uScA8XEJDGnxSY5kM+qtGgv+nGDU29CkKygn/DiY6e8sITwogo6vNafb4Na4ebvw1fYPOLj1JDPeyajD+tHPdPAPjU9TxYINw4mNvcO095POyZvw9atMH/sb4SFRLJiygVGTe9B7aGsCTl9h03Ljth9NWlejZafaxMXFczs2js+H/y/LfSbEJzDny9+ZOLsXJnsTm1b9xb//hPBsV6Onbd3yQxzY/Tc+T1bk+1VDjdumjF+Z6boAg997hjx5HPjsK+Oq7zMnLjHzs7UAVK9TmtDgSAKTnUOZkQO7zuLTpCIL1g7jVuwdpo77LalOZvdi+kcrCQ+J4rvpmxj9ZXdeHdySgDNX2bjiMABuHk7M/GkgBR3zkZiQSOdXGvFGl1nE3LjFqM+7UaNeWVxcC7J40wiWzN3GxhVHMq6rhERmzt7MlxO7YzLZsX7jCc7/G8pz5gbxmnVH2X/gHxrUL8eSha9z61YcX0xOujtEvnwO1K1ThqnTN6TY7vqNx3l3+DMsmNePO3fi+XzSuizrRYhHWi7q6bOz5oo5a9w9J04p9TzwBsYFDO7AIYyHBL8IlNdav2V+Ttw2oKx59bVa62rm7YwAnLTW4zPYTyhQBWN49nfgcrJz79ZqrZeZzxNMvs2FGOfT7QH2kzS8uxGYZT6vzh94DtDAr0BU6u2mep91gala62aZ1ElXoKPW+lXz64mAC/CW1jpRKVVba/2XufGafBg49evz5tehqZdlIBGgfZE3Myny31gf+JVlup3bPd1O6IHYcG2+Zbp94UE2TGJYH5R0P8OHIQ+kylRljA2TGNafmmiZblv3QxsmMWw8/JFlul3NsTZMYthw7GPLdPM2X9gwSZLtm96zdQSRO9xPJ0uOafjK1Bxv9u1f8o5N3tuDGN5dARwHjmE07N7VWgdiDKXWM98xuieQ3fGMscCfwOZ73YbW+iowGthuzndEa73KvHgURsNwG5D2bqppHQfilFLHlFLDMiizHahivlP2i8DHGBexHFdK+ZlfCyGEEEI8cDnW0yceKtLTlwHp6cua9PRlTnr6siY9fSKHPBw9fT2n5HxP34/Dc01PnxBCCCGEeMg8tM/eVUrNwbjlSnIztNbfp1felpRSbTFuBZPcOa11F1vkEUIIIUTOyE23bHloG31a68G2zmAtrfVGjItChBBCCJGb5KJGnwzvCiGEEEI8Bh7anj4hhBBCCFuzS7B1gpwjPX1CCCGEEI8B6ekTQgghhMhILjqnTxp9QgghhBAZyE1X78rwrhBCCCHEY0B6+oQQQgghMpKLnlwmPX1CCCGEEI8BefZu7iQfqhBCiEfdQ/Hs3SZdJ+f479Tdy0fIs3eFEEIIIcSDIef0CSGEEEJkJBeNnUmjLxdr03CCrSOwaf84y3Trxp/YMIlh894PLNPtK75nwySG9We/sEy3Lz/ShkmSrPefZJluX2WMDZMY1p+aaJl+GOooef20rfuhDZMYNh7+yDLdvvJoGyZJsv70Z5bpNo0+tmESw6Z9Y20dQTzC5JYtQgghhBDikSI9fUIIIYQQGclFF7xKT58QQgghxGNAevqEEEIIITKQm87pk0afEEIIIURGclGjT4Z3hRBCCCEeA9LTJ4QQQgiRgdw0vCs9fUIIIYQQjwHp6RNCCCGEyEhC7unqk0afEEIIIURGck+bTxp9j5N6DcsxaFhbTCYTG1b/xc+L96Qp8+Y7bfFpVIFbt+4w+eNV+OtAvLxdGPlhZ9w9HElISOT3lUdY+csBAF59/WkaNVUkJiRy/doNJn28ivDQaOvyNHiCN99ui8nejvVrjvLz4r1p8wxrQ/1G5bkVe4dJn6zB/6yR592xHXH3cDLyrD7Cil8OAlCuQmGGjmxP3rwOxMcnMHPyBvTpK1bX0cAPOuLTTHHr5h2mjPqFgFNp1y1cwo1R017GuVBB/E9dZvLIn4m7E29ZXrF6Cab+MpjP317K7o0nyJPXgUlLB5Inrz329vbs3niCJTM3W5dnbCd8nq5k5HnvZwJOXk4/z/RXcHYtgP/Jy0we8T/i7sRTvcETfPh1HwIvXgNg76YTLJ29xcjz0yDy5HXA3sHE7g0nWDJjk/V1NKYDPk0Vt27eZsqY5QSkU7+Fi7sxakoPnAsVwP/UFSaP+pW4O/E071CTbv2bAnAz5jazJ6zinA6keBlPRk/tYVm/aAl3Fs/awsp0jomHrY7qNSrPwBHtsbe3Y/3KI/yycHeaMoNGtqf+kxWIjb3DlPEr8T9zNdN1nV0KMOazbhQu5krQlet8OuoXoqNicS5UgLFfvkjFKsXYvOYoc778Pev6GfOc8XnF3mbKmGXpH9PF3Rg15SWjfk5dYfJ7vxB3J56GLSrTe0hrEhISiY9PYN5nazl55F+jfha/nlQ/G/1YMntLllnqNSzHIPPPvPEdlN7PfFt8Ghs/85M/Xm35mR85rlPSz/yqVN9BT1VM+g76ZLXV30FCPG6k0feYMJns8B3RnlFDlhAaHMms719j3y7NhfOhljI+jcpTvKQHfbvNplLV4gx591mG9P/O+LKfuQl/HUiBgnmZs3AARw78w4Xzofy6ZC+L5u0AoHP3+rzSrykzrfhFZDLZ8daI9rw39EdCgyOZ/V1/9u06myJP/UblKF7CnT7dv6Jy1eIMGdmeIQO+Jz4+gW9mbcH/rJHnqwX9OXzgHBfOhzJgcEsWL9jFwf0B1G9UjgGDWzLCd7FVdeTTTFGsjCf9W0+iUs1S+H7UhWHd5qQp12/EM6xcuJud647h+1EX2r7gw7qf9lveV98R7Tmy+6yl/J3bcYzqPY/YmNvYO5iY/NMgDu3UnDl2IYs8lYw8Lb+gUq1S+H70PMNemJU2z7vPsvL7P4w8E56nbbf6rFu6DwC/g+cY//r3KcrfuR3HqF7fJOX532AO7TzDmaOZ5wHwaVqRYqU96N9uCpVqlMT3w04M6zE3babh7Vi5aA871x/H98NOtH2+Hut+/pPAS9d499VviY6Mpd5TFRnyUReG9ZjL5fOh+D4/21KHi3eMYu/WU1nnsXEdmUx2DB71LKPf/IHQoEhmLX6d/Ts1F86FJGV8soLxc9V5JpWqleCt0R0Y+uq3ma7bvU8T/jr4D78s3E33Pk14sc9TfDdrM7dvxbFo7jbKlPOmTDlvKz4vZf68JlOpZkl8x3VmWI+v0v+8ftjNzt+P4/thZ9p2rce6//3J0f0B7N92GoAyFYswZtpLvP7sNKN++s5Pqp8lAzm0S3Pm2MUMs5hMdvgOb8co88/8rAWvpfmZN76D3OnbbY75O+gZhry2wPwdtNnyMz/n+9fS/w7q5mP1d5AQ1pILOXIBpVS0+f9iSqll5ulaSqlnslhvvFJqRAbLMu2WUErZ7On1qkpxrly6RuCV68TFJbBz80kaN1UpyjRuqtj8+zEAzpy8jKNTPtw9nAgPi8ZfBwJG78yF86F4ersAEBNz27J+/vx5rO4FV1WKceVSuCXPji0nafxUxRRlGj2l2LLhBACnT17GySl/Up6zyfL8G4qnlzMAiYmJFHTMB4CjU37CQqOsrqOGLauydcVh4/0fu4CTcwHczNtNrmajcuwy59qy4jCNWlW1LOvY60n2bPLjeljKnoZYcz05ONjj4GBPohWP9WnYKlmeoxdwcsmffp6G5VPmaV01TZnUUuTJY7IqD0DDFlXYuuovI9Pxizg558fNM51MDZ5g1yY/I9PKIzRqWRmA00cvEB0Za6x/7AKehV3SrFurYTmuXggn+Mr1rPPYuI5U1eJcuRhO4OVrxMXFs2OTH42erpSiTKNmldiy7qiR0e8Sjk75cfd0ynTdRs0qsWWtsc6WtUct82/F3uHk0Qvcvh2XZX6Ahi0qJ31exy5mUj/l2LXR/HmtOkKjllVS1AFA/oJ5UzyNKm39ZJ7F+JlP9h20Jb3voIpsXn/cyHvyslFX6f3Mn0/6mU/xHVQgr9XHshCPo1zV06eUctBaW/dtaKa1vgK8YH5ZC6gHZOvPRK114yyKjAEmWrs9pZQdYKe1TshOnuQ8vZwJCY6wvA4JjqRS1eIpynh4ORMSHGl5HRochYeXM+HJGjCFixaifMUinPG7ZJnXZ2BzWrevwY3oW4wc/IP1eYKS7SskikpViqUpE5yiTCSeqfMUKUT5CkU4Yx7Smzt9E59Ne5nXfVthMsHQNxZZlQfAo7ALoYFJdRQaFIFnYReuhSQ1HF3cCnIj8iYJ8cZHEhoYgYe54eJR2IXGrasyqvc8KlZ/IcW2TSY7Zq4YQrFSHqz9cR/6eMY9IinyXE1q+IQGRuBZuFDaPFHJ81zHo3Ahy/LKtUszZ80wwoIjmf/5Wi78HZSUZ+XbFCvtwdole9GZ9NCkyOSduo4ijTpK1rh2cS3IjajYpExBkSky3dW2az0O7TqbZn6zZ2qw0/zHR5Z5bFxHHt4uhASlPGYqVSuRooynd6pjPTgSDy+XTNd183C0DFGGh0bj6u5oVX2kyVe4EKGBqerHO9Ux7VqQG5Gx6R7TAI1bVaHPsLa4ujsxblDSz5PJZMfMZb7GMf3T/iyPaU8vlxTfLxl+B6X6mU/zHVTE/B2UbBi/zxvNad2+uvEdZGXPvhBWy0V/SDxyjT6lVG9gBMaplceBeCAcqA0cUUp9BcwBvIAYYIDW+oxSqiywFOM9b0i2vTLAWqAOMAEooJRqAnymtf45gxhVlFI7gFLAdK31TPO2orXWTkqposDPgIt5f4OAZ83bPgqc1Fr3VEq9A/Qzb3O+1nq6Oc96YDvQCFiplHLVWg8z72MAUFlr/c49VZxd2lmpD2M7u8wL5S+Qh3GfdWPu9I0p/rpe+PV2Fn69nR69n6TjCz4snr/Tijhp95X65yrdOMkK5S+Qh3ETX2DujE2WPB2er8vcmZvZveMMTVtUZvjoDrw39Mcs82S8P2tyG4XeGPMcCyatJyGdK70SEhLx7TQDR+f8jJ3Tm9IVCvOvuXGRcZ6M92VNmYCTl3m12URiY27j06wS4+a+ymutvkzK03GakWfuq1blMfaXdt69ZLqrRv0naPN8PUa88k2K+Q557GnQvDLfT7PuHENb15E1x0x6hRITE61b9z5l//NKmt675RR7t5yiWr0y9B7SmjH9vgPM9fP8LKN+Zr2S9TFkTZb0C1kmk76DNqX8DvpmOwu/ubfvICEeR4/U8K5SqirwPtBCa10TGGpeVBFopbUeDswD3tJa18VoHN49gWUGMFdr7QMEpt621vo2MA74WWtdK5MGH0AloC1QH/hQKZUn1fKXgY1a61pATeCo1noUcNO87Z5KqbpAX6AB0BAYoJSqffetAj9orWsDk4GOyfbRF0h5ApIVQoOj8PJO6t3w8nYhPCQqVZlIvLyT/sL39Ha2DI/a25sY91l3tm30Y8+OM+nuY9smP55qXtmqPCEhkXgl603w9HJOMxQbEhyFd4oyLoSZez/s7U18OPEFtm3yY/dObSnTpn0Ndpvz/bHtNCpV72FqHXo2YvaqocxeNZSw4Cg8iyTVkWfhQoQl65kAiLh2A0eXApjsjR8dzyKFCA82cleoVoJR015i4bb3aNK2OoPHd6ZRqyop1r8RFcvxA/9Q76mUw1qWPK80ZvbqYcxebfQ8eRZ1TcpTJJ084TdwdE6ex5Vwc5mY6FuWIbiDO8/g4GCPi1vBtHn+/Id6TVMOSabI9FJDZv/my+zffNOpIxfCglN+bv9n77zDoyjeOP7JhQ5JKAm9Shk6iEQIXXqkWZEiTVEpURRBA1iwIQjSRMEGKirSFCnSpRMEpbdBqnQSAoQEAqT8/tjN5e5yKfCD7AHv53nyZG53Zvd7s7Nz777zzuylCzHk9smRrKmQr10TGLFhr77/OO+HTOfypatOZWs3rMChvadSDI97ah1FnI0ioJBLm3Fpx0Yex/vKl8iIy2mWvXA+hvz+eQDI75+Hi5ExqdaHK2271GXSry8z6deXjfop7FI/4W6ul28OlzbtXIcAu/8+SpES+fHN66Z+Nh+hdoMKKco44tq/BBT0TTHhIiJFv+B8z78z4mn+XLqLDWvS6IOapN6WBeFW8Eq8/X9WcVcZfUBTYI7WOgJAax1pbp+ttY5XSuUB6gGzTY/al0ARM099YIaZ/n/9/4u01tdMHeeAQi77twC9lFLDgWpaa3eBZQ2A37TWMVrraOBXoKG575jWepP5HWOAP4G2SqmKQFat9a6bFaz3naRYifwULpKXLFlsNG5RhTCXobWwdQdo8WgNACpWKUZM9DX7sMrAYe3472g4c80JC0kULZHfng5qWIHjxyLICHrfKYoVT9bTpHkVwta76Fl/gOatqwFQqUoxYmJi7XpeH9qW/45GMPeXv5zKnI+IpvqDpQB48KHSnDweSVos/CmMkA4TCOkwgbAVe2j2+EPG969RkpjoWKdhsCR2bjpEQ1NX88cfImzlHgB6NRtFz6bG3/qlu/h8+DzCVuzFL19ucvvkACBb9iw8WK8cxw+fc6/nx42EtB9HSPtxhC3fnaynZkliLqei56+DznpWGHocY+0qVC+Bl82LqAtX8MufcT0AC2dsIuSJSYQ8MYmwlXtp1sF4NqlYvYShyU3c5M7Nh2nYsqqh6bFahJmTAQKK+PH2xK6MDp3NyWPnU5Rr8mgNVqcztOtJdaT3nkxZWx0AACAASURBVKJYifwUKpqXLFm8adKyKptcDJJNa/fTvE1NQ2PV4lyJjiUyIjrNspvWapq3Nco0b1uTsFSMHLf18/MmQp74jJAnPnO+XjVKpFE/h2nYyrxeHZKvV5GSBex5ylYuSpas3kRdvJKyTQeV5bjD5BV36H2nnPug5qn0QcHVDb0u9/zAYe3471jKe75ocYc+qEEFjrtpV4Lwf5F4B/4s4m4b3vXCfXUlPQbbgIumh80dt6uqrzmk43GpR631WqVUI4wh3elKqdFaa9dgNzfjGHZcH+u/wYgH3M8tePkAEuITmTRmMSMmdMVm82Lpwu0cOxJOG/MHc9Fv/7B54788XK8c380JMZZL+HA+AFVqlKDFozU4fPAsk394EYCpk/9kS9hBnu/XjBIlC5CQmMi5M5eYMGpRxvWMXcLH4zpj87aZeiJo+1gtABbO28rmjQepE1SO72f3N/R8tMDQU70ELYKrc/jgWaZ819vQ8+UqNocdYuzIRfR7tSXe3jauX49jfAb1AGxZvZ/AxoqpK94g9up1xg2Zbd/3/te9GD9sDpHnLjN1zGJCx3Wh+6stObT3FMtmb0nzuPkK+jBoVEdsNhteNi/WLd7J5lS8pSn0NKnE1D9DDT1vzkrW881zjB86h8hzUUz95A9Cx3el+8DWHNp7kmWzjaUsGgRXo02XIOLjErh+7QYjzWHufAG+DBr9TLKeP3awedW+jNXRWk1gI8XUJa8TG3uDccPmJmua0oPxb/9KZPhlpn66hNAxneg+oAWH9p1i2dy/AejStyk+frno/057AOLjEhjQ0XDGZ8+RlQfrlWPi8N8ypMUT6ighPoHPP/mDEZO6YfO2sez3bRw7HE6bJ2sDsGju32xe/y+B9Ssw7fcBXDOXbEmrLMDM79YxbGRHWneoxbkzl/jI4Xt9v+BVcufOTpas3ikmjaSonzXm9Vo6yLheQ+ck18+XPRn/1lzzei0m9NPOdH+lpXG95hhtukHLKjTrUIu4G/FcvxbHyIEzzPrxYdDHT2Pz9jLqZ8mudNt0Qnwikz5dwojxXcw+aIfZBxn3/KLfjHv+4Xrl+G52f65di0vugxzu+cnfvwDA1CmrzD6oqXMfJDN3BSFVvO6mmU7m8O5vQJDW+rxSKj8wFliotU6agbsRGKe1nm1OhKiutd6hlJoPzNJa/6iU6guMNuPvSpvlqyqlngTaa617pKFhOBCttR5jft4NtNVaH3WI6SsFnNRaxymlXgVKa61fVUpdAApqrW8opWoB32EM7XoBfwHdgAtJelzOuxUjTrG61vpCOlWVCNCy7vsZqdY7yrJN79jTLep9aKESg+Ub37Kngyu8aaESg8UHRtnTweUGW6gkmcUHR9vTwZUtm3BuZ/He5LlPnlBHjvXT6qF3LVRisPSf9+zp4EpDLFSSzOJ9H9vTLYM+sFCJwbKwt62WINwaaTlHMo2mLUbedkPpz+Whlny3u2p4V2u9B/gIWKOU2oFh8LnSFXje3L8H6GBuHwD0V0ptAVJOJTRYhTFJY7tS6pn/Q2oTYLtSahvwJEY8IRjxhjuVUj9prbdiGH2bMQy+b7TW29I45ixgQwYMPkEQBEEQhBTcbcO7aK2/B1Jdh0NrfQRoncr2IIdNI83tR4GqZjoSCEzn/MNdPld1SOdJS6PW+k3gTYfPY3ExXB31uNAAGJeWNkEQBEEQbjP/96JpnsNdZ/Tdbyil8mJ4A3dorVdarUcQBEEQ7ie87qIwuPQQoy8VlFK9SF4SJokNWuv+malDa30RY0kaQRAEQRCEW0aMvlTQWk/jFmfKCoIgCIJwj3DvOPrurokcgiAIgiAIwq0hnj5BEARBEITUkJg+QRAEQRCEex8rX5t2u5HhXUEQBEEQhPsA8fQJgiAIgiCkxj00vCuePkEQBEEQhPsA8fQJgiAIgiCkgtc99EYO8fQJgiAIgiDcB3gl3kNj1YIduaiCIAjC3Y6X1QIAWtT/8Lb/pi7f8JYl302GdwVBEARBEFLjHnKjyPCuIAiCIAjCfYB4+u5hWj34rtUSWLrtPXu6Zd33LVRisGzTO/a0p9VPcIU3LVSSzOIDo+xpT9DkpKfcYAuVGCw+ONqebvWQB7ShfxzaUIkBFipJZvHxCfZ0cOnXLFRisPjoOHva066Z4Pl43UNhcOLpEwRBEARBuA8QT58gCIIgCEJq3EOePjH6BEEQBEEQUkPW6RMEQRAEQRDuJsTTJwiCIAiCkAr30kQOMfoEQRAEQRA8DKVUa2AC4A18o7Ue6bLfy9z/KHAF6Km13prWMWV4VxAEQRAEITUSE2//XzoopbyBz4FgoDLQWSlV2SVbMFDe/HsRmJzeccXoEwRBEARBSA0LjD7gYeCg1vqw1vo68AvQwSVPB+AHrXWi1noTkFcpVSStg4rRJwiCIAiC4FkUA447fD5hbrvZPE5ITJ8gCIIgCEJq3IElW5RSL2IMySbxldb6K4fPXm6KuboIM5LHCTH6BEEQBEEQMhHTwPsqjSwngBIOn4sDp24hjxNi9GUApVQT4LrWeqPVWm6Wvm8E83D98sTG3uDTd+dxcP/pFHkKFc3L0JFP4+OXk4P7TvPJW78SFxefZvmB73agTqMKXIyM4aWnv7Af64EKhXllWFuyZc9CfHzaj0e165al72utsNlsLJm/jZnTN6TI029gKwKDynPt2g3GfPA7B/UZAgr6Mvjdx8hfIDcJCYn8MW8r82ZtBqBh00p0692YkqUDePm5b/jXzffNjDrKmi0Ln37bi6zZsuDtbWPdir1Mn7LKfrz2nerQ/pmH09XW5632BDZWXLt6g09DZ3Fob8r7uVDxfISO64KPXy4O7j3JmMEzibsRb99foVpxxs7qz8hXf2b90l0AvDbiKR5+pBIXz0fTt+24FMe8mzT1ebsDgU0qGnrenMmhPSfd6xn/LD55c3Jwz0nGDPqFuBvxVKvzAO9O6cmZ4xcA2LhsFz9PWkHWbFkYPaOvcf2y2Fi/ZBc/TliW4rjT5r3C4nlbmfXd+hT7+g52aBfDk9tV7aBy9BkUjLe3l1NZH9+cDP34aQoVzcvZUxf5KHQW0ZdjeSS4Gk93q28/bpnyhejf9UsOHziTsfp57wkCm1Y26mfgTxzafSJl/ZTIT+jnPfDJm5uDu48zZsCPxN2I58mXmvLI4w8B4J3FmxLlCtGp5jCiL17hu43vcCXmGgnxCcTHJzCgzacZ0gPQ593HCXykkqFp0AwO7XGjqXh+Qid1N9rQnhOMee0nexuqVrcsL73zOFmyeBN1IZo3nvkcgNy+OXh1ZCdKqcIkJsK4N2awf+sxp+N27Nngjl4vVaUYA4a1A8DLy4vpX61i46r9AHz02bPk9/fJcD0JnoFFS7ZsAcorpcoAJ4FOQBeXPPOBEKXUL0Ad4JLWOs0fvXsmps+c6XInjpsFaALUuxPHdzjHbSewQXmKlSxArw4TmfDhAl4e2tZtvt4DWvDrT2E812Ei0Zev0vrxWumWX7ZgO8P6/5jyWK+24MevVtOv0xR+mLwqxf4kbDYvQgYFM+y1n3mh8xc0aVmFkqX9nfUHlaNYiQL0enoS4z9eyCtvtAEgPj6BryYuo3enyQzoPZX2TwXayx49HM77obPZtf1YinNmZh3duB7HGy9+T99nJtO302Rq1ytHxWrFAahRuzT1mij6dvzC7bns2horipb25/kWo5n49q+EvPe423zPDXqUed+tp3fL0URfukqrpwLt+2w2L3oNCmbr+gNOZZb/+g9vPf9thurIkzUFNq5o6Gk2iolvzSHkvSfc63mjDfOmraV3808MPU8nG9y7txwhpP04QtqP4+dJKwDj+oV2+5L+7cbRv904HmqoqFizZIrjvvDU5zzSqholywQ466pf3mi7j5ntYkhb+3fvH9qGt175MUXZjj0bsG3LYZ57fCLbthzmmZ4NAVi1eBf9ukyhX5cpfPLOr5w9dTHDBl/gI5UpWiaA5xt+yMQ3fyFkxNPu62dIe+Z9s5rejT4k+uJVWnWqC8DcL/8kpPVoQlqP5ruRC9i16SDRF6/Yy4V2nERI69E3ZfAFNqlkaGoygolDZxHy0VPuNYW2Y963a+j9yAjjmj1TBzAMu5APnuK93t/Qp+UoPur3vb1Mn3ef4O81+3ix2Uj6B4/m+MGz2GzOI2B3+nodPXSOkG5f0a/LFIa9PJ0BQ9th8zZ+aj8KnU3fzulOsBQEtNZxQAiwFNgHzNJa71FK9VFK9TGz/QEcBg4CXwP90jvuXWH0KaVKK6X2K6W+V0rtVErNUUrlUkodVUq9o5RaDzytlOqslNqllNqtlBrlUD5aKfWpUmqrUmqlUirA3F5WKbVEKfWPUmqdUqqiuf07pdRYpdQqYCbQB3hNKbVdKdVQKXVEKZXVzOtr6siaivYXlFJblFI7lFJzlVK53JxjVBpa2iml/lJKbVNKrVBKFcpovQU1rsiKhdsB2L/rBLl9cpDfP0+KfDUCy7BuxV4Ali/YTlCTiumW3731GJcvXU1xrMREyJ07OwC582RPVZuqXIxTJy5w5tRF4uISWLN8D/UaKac89Roplv+xwzj/npPkzpOd/AXyEHk+moPa+NG7euU6/x2NwL+gLwDHj0Zw4r/zGayhO1tHsVevA5AlizfeWWwkmk+LbZ8OZOa09dxw8Hy5o26zKqz87R/j2Dv+I49PTvIFpPQS1Agqy7olhrdsxW//ENS8in1f+2712bBsNxfPRzuV2f33EbfXLz08TVPd5g56tv9HHt8c7vXULeesp0WVFHlcib2SfP2yZE2+fo7ExcWzetlue3tIIqhxRVYsMtvF7hPkzmO0C1WlGKeOR3Lm5IUUZR3b0oqF21McEwyDZfXS3fbPOXJmS/M71G1ZlZVztxg6th0jj29O8pn3iiM16pdn3SLjXlsxZzNBraqlyNO4w0Os+T3NJcAyRN2WVVn5q4Mmn5zkC3CjqV451pn3/4q5mwlqaWhq0v4hNizZSfipiwBcMttRrjzZqfrwAyyd+RcAcTfiiYmKpYKLsX6nr9e12BskmKMcWbNlcZqoeSXm2q1UmWA11szeRWv9h9a6gta6rNb6I3PbFK31FDOdqLXub+6vprX+O71j3hVGn4nCCHSsDkSRbNHGaq0bAGuBUUBToCYQqJR6zMyTG9iqta4FrAHeNbd/BbystX4IGAQ4ul4qAM211k8CU4BxWuuaWut1wGqgjZmvEzBXa30jFd2/aq0DtdY1MKz1592c4/U0tKwH6mqtH8SYsv1GBuoKAP+CPoSfibJ/jjgbRQGXDt83by5iLsfaO6mIs1H4F/TJcHlXpoxZTO9XW/Lj4oG88Fqr1LUF+BB+7pL9c/i5KAq4/FgXCPAh/JzD+c9dTpGnUBE/ylUozH43Q1YZ4U7Wkc3mxRe/9GHmysFs23QYvdsYdixWqgBVHyzFhB9eSFNbgUK+RJxJrqOIs5fwL+SiLV8uYqKuJms7c4kCZp4ChXyp16IKf8zYlPEKSQdP01SgkC8Rpy8m6zlzCf9Cfin1XHbUc5ECDnkqPViKzxe8xvvfPk/J8snPVDabF5Pmv8aMv95l2/p/0TuO446Is5fwd2mX/gV9CD/r2HajKBDgS4GCvoSfveS2bL4CuYmMMAyYyIho8ubPneJcjVpWZZU5HA7Qo2/TVGrGoEDhvESccqif05fwL+xaP7mdr9fpixQonNcpT/YcWandpCLrF++wb0tMhI9+6svERYMI7hKUpg4nTYX8nDWduZgBTZfs16z4AwHk8cvFqF/6M3HBQJo9URuAwiULcOl8NAPHdGbSotcZMPIZsufMhn8h5++SGddLVS3GV7P68+XMfkz8eIH9ewB8NKlbhutK8BAsMvruBHeT0Xdca50U9PUj0MBMzzT/BwKrtdbhplv0J6CRuS/BId+PQAOlVB6MIdvZSqntwJeA4/o2s7XWqblivgF6melewLQ0dFc1PXe7gK6Ao4thttY6Ph0txYGlZvnBLuXTxivlxB5Xb4WbLMntMQPlXWn7dCBffrqEZ4PH8uWYJWloc3Ne1yxuxSUnc+TMyjsfP83k8Uu5Ynplbpo7WEcJCYn06zSFrq3GoqoWo1TZggB4e9vI45uDAd2/vllpKfoKLzcVmXT+l4a2Y+roxSQk3L4OxtM0uWsjKa9f6nkO7TlJj8Yj6N9uHAt+2MA7k3vY8yQkJBLSfhzdGnxIhRolKFU+dSd7itsilXNmpP5SQ1UtxrXYGxw7dA4w4meLlsifZhn358tI+3bOU6dFVfZuOeI0tPv6E+N5+dExvN19Cm17NKRqnbIZ+h4Zu2apa7J52yhfrTjv9Pqat7p/SeeXW1KsTADe3t6Uq1qcRT9uIKTNp8RevU7Hvs3c9zV3+Hrp3Sd5sePnvNztKzr1bEjWbMkRPMNCpqd/AEG4Q9xNEzlcb7WkzzHmf3dTl9M6lg24qLWumUqemFS2o7XeYA45Nwa8tda7U8sLfAc8prXeoZTqiREf6HqOtLR8BozVWs83J5QMT+NcAP2TEpHhlwkonOyF8S/kS2T4ZafMly5cIbdPDmzeNhLiE/Av5Mt5M0/E2ah0y7vSom1NJn+yGIC1y/cwLJV8EecuE1Aw+ek+oGDKY0eciyLAwevmX9CH8xFGHm9vG+983JE/l+5mw+r9aWpKjS9+6cOBPafueB3FRMey4++jBNYrx7FD54g4G8WGlfvcamrbNYjWHY14swO7Tjh5QPwL+XHewfNpaIsht2/OZG2F/Yg8Z5y/fNXihI7rDBiek8DGFYmPjyfMHKbOKJ6mqe2z9WjdsY6p5zj+RZI9Of6F3eiJjCG3j6OevESaea5EJw+3bVmzn/7vPY5vvlxEXUg2bmIux7Lzr8PUblSRY/+eTaHHv5CfvV0mEXE2ioBCjm3Xl8iIy2TN6k1AIT+3ZS+cjyG/fx4iI6LJ75+Hi5HOXVCTltVYvSTZy1e5egnKV3Jeh3XUrBDWLdpO686G5+3Ajv/wL+pQP0X8OH/WTf04Xq8ieYl08G4BNG5fi9XznYd2I83jXDofzcYlO1E1S7L7r0Mp6gegbbf6qWsqnDcDmvzs1yzizCWiLsRw7ep1rl29zu7NhyhTqSh7thwm4swl9Pb/AFj/xw469m3GltXObSuzrhcY4SaxsTcoXbYg/+5Lc1Kl4MncQ+/evZs8fSWVUkljCJ0xhj0d+QtorJTyNyd1dMYYygXjeyZFC3cB1muto4AjSqmnwXiHnVKqRirnvgy4Bgr9AMwgbS8fZrnTZsxfV3cZ0tHihzFzB6CHu/IufJ6U2LhqH83bGnZkxWrFuRIdax+OcGTH30dp2Nx4u0uLdjUJM42oTWv2Z6i8I+fDL1P9odIA1Hy4TKr59L6TFCuRn8JF8pIli43GLaoQts45sD9s3QFaPGpUQ8UqxYiJvkakGb8zcFg7/jsaztz/Y5iwX6cpd6yO/PLlIneeHABky56FWnUe4PjRCAA2rt6fat0s/CmMkA4TCOkwgbAVe2hmzpysWKMkMdGxXHBjdO/cdIiGrY14p+aPP0TYyj0A9Go2ip5Njb/1S3fx+fB5N23weaKmhT9utE+8CFu+O1lPzZLEXE5Fz18HnfWsMPTkc5hJWaF6CbxsXkRduIJf/tzk9km+fg/WK8fxw+dSHDdLFm+atKzKpjXODx6b1u6neRuzXVRNbhd67ymKlchPoaJ5U5TdtFbb21LztjUJcziml5cXDZtXZvWy5OfLhXO20KW18wSKNztOYuH36+2TL8KW7qLZk8YkmooPljLqx8UoBti58V8atjHuteZPPUyYw3ly+eSgWt2yhDkMK2fPmY2cZuxu9pzZqNWoIkfTmDS4cPoGQh4dQ8ijYwhbtptmTzhqusqFcDeawg7S0Lz/mz+ZrGnTsl1UDXwAm7eN7DmyomqW4vjBs1wIv0z4qYsUe8CYaFGzfnn++/cMB1yG5e/09SpUNK994kbBwn4UL1WAs6cvkiNnNrfxwoKQmdxNnr59QA+l1JfAvxjvmHs5aafW+rRSagiwCsPr94fW+ndzdwxQRSn1D3AJeMbc3hWYrJR6C8iKETOXHLSSzAJgjlKqA0bc3TqM4eMPMQy/tHgbwyA9BuwipfGYRGpahmMM+54ENgGpW1IubF7/L4ENKjBt/gCumcsQJPHBZ10Z9/58IsMv8+2E5Qwd+RQ9+zXloD7D0nlb0y0f+vFTVH+oNH55c/HjkoFMn7KapfO2Mv6D+fQdHIx3FhvXr8Wlqi0hPpFJYxYzYkJXbDYvli7czrEj4bQxf8AX/fYPmzf+y8P1yvHdnBCuxd5gzIfzAahSowQtHq3B4YNnmfyDsbbl1Ml/siXsIPUbK/q9Hoxf3lx8OLYzhw6cZeirP2V6HeX392HQ+49js3lhs3mxdvke/jKN2qXztjFweAe+nJ32RKstq/cT2FgxdcUbxF69zrghs+373v+6F+OHzSHy3GWmjllM6LgudH+1JYf2nmLZ7C1pHhfgzbGdqf7wA/jmy830tUOZPnE5y+akX87TNG1ZvZ/AJpWY+meooefNWcl6vnmO8UPnEHkuiqmf/EHo+K50H9iaQ3tPsmy2scRPg+BqtOkSRHxcAtev3WDkAKOt5AvwZdDoZ7DZbHjZvFj3xw42r0rpnf16bgjLft/GscPhtHnSiC1bNPdvo13Ur8C0353bRUJ8Ap9/8gcjJnXD5m2zlwWY+d06ho3sSOsOtTh35hIfOXyXarVKEXEuijMnL6Rbj0718+deAptWZur6t436ef3n5Pr5/iXGvzGDyLNRTP14AaGf96D74DYc2n2CZb+E2fPVa12drWs1164mh1DkC/Dh7a+N8GRvbxurf/+HfzLocd+yai+Bj1Ri6pphhqbBvyRrmvYC49+caVyzkQsJ/awb3V8P5tCekyybZTzgHT90jr/X7GfyksEkJCSydOYmjpmzmScPn8sb47uRNas3p4+fZ9ygGU7xdGCMQNzJ61W1Zkme6dmQuLh4EhIT+WzkIqIuXiFv/twMH9uFrNnuyEITwp3kDizObBVe6cVoeQJKqdLAQq111VssH621vq2PWEqpp4AOWmtPjMpNBGj14Lvp5bvjLN32nj3dsu77FioxWLbpHXva0+onuMKbFipJZvEB+8R3j9DkpKfcYAuVGCw+ONqebvWQB7ShfxzaUIkBFipJZvHxCfZ0cOnXLFRisPho8rqPnnbNhDS5mbCtO0Zw5aG33VBavHeEJd/tbvL0eQxKqc+AYOBRq7UIgiAIgiBkhLvC6NNaHwVuyctnlr+tXj6t9cuu25RSnwP1XTZP0FqnF/MnCIIgCIKncheMiGaUu8LouxvQWvdPP5cgCIIgCII1iNEnCIIgCIKQGrdxrVOrEaNPEARBEAQhNe6h4d27aZ0+QRAEQRAE4RYRT58gCIIgCEJqiKdPEARBEARBuJsQT58gCIIgCEJqiKdPEARBEARBuJsQT58gCIIgCEJqyJItgiAIgiAI9wGJCVYruG14Jd5DY9WCHbmogiAIwt2Ol9UCAILLDLztv6mLj4y15LuJp08QBEEQBCE17iHnmEzkEARBEARBuA8QT989TIt6H1otgeUb37KnG7UfbaESg7XzB9vTrQLfs1CJwdIt79rTwcVfsVBJMotPTLSnW1cZZqESgyV7PrKngyu8aaESg8UHRtnTwSrUQiUGi/VIezq4zEALlSSz+MhYe7p1zXcsVGKwZPv79nSZnz62UInBka5D7OkWDT5KI2fmsHy99fe5RyMTOQRBEARBEO4DZHhXEARBEARBuJsQT58gCIIgCEJqiKdPEARBEARBuJsQT58gCIIgCEJq3EOePjH6BEEQBEEQUiPh3nkjhwzvCoIgCIIg3AeIp08QBEEQBCE17qHhXfH0CYIgCIIg3AeIp08QBEEQBCE1xNMnCIIgCIIg3E2Ip+8+onadB+j3aits3l4sXrCdmdM3psjT77WWPBxUjmuxNxj94QIOHjhD1mzejP2iO1mzZsHb28a6Vfv44du1ADR6pBLdnm9EydL+vNx7Kgf2n74lbQ/XKs0rvZth8/Zi0bKd/DR3s9P+ksXyEzogmAplC/LN9PX8Mm+Lfd+br7SmXu0HuHDpCj1f/u6Wzg9QO6gsfV5vjbfNxuLftzLr+w0p8vR9vTUP1y9PbOwNPn1vHgf1GQAGvt2eOg0qcPFCDC91mmzP37BZZbq92JgSpQN4pefX/Lvv5uqnz/tPEti0MteuXufT137i0O4TKfIUKpGf0C964pM3Fwd3nWDMgOnE3YjnyT5NeeTx2gB4e9soUb4wnWoMJfriFTo835jWnYPw8vJiyc9hzPt2dYY19R3ShsBGimtXb/DpsLkc3HcqpaZi+Rgy5hl8/HJycO8pRg+ZQ9yNeB5pU4OOzzcC4OqVa3z2wXyOmHUIYLN5MXFWP86fjeLd/tMzVkdvtSewsakndBaH9rrRUzwfoeO64OOXi4N7TzJm8EzibsTb91eoVpyxs/oz8tWfWb90FwDf/fkmV2KukZCQSHxcAgOe/CzDdWTXNqydoS32Bp+Gzk5d29jOydremJVS28x+jHztZ9Yv3X3zGt59nMAmlbgWe51PB83g0J6TbjTkJ/SzboaGPScYM/Bnu4Zqdcry0juPkSWLN1EXYnij0+cAvDbqGR5uWpmL56Pp2zrt92r3feNRAhuUN+rhnd846KafKFQ0L0NGdTTazL5TjB72K3Fx8WmW//6P17gSc52EhATi4xJ4peuX9uO171SH9p3qEB+f8ZmXjYo8wLu1m2PzsjHz4Ham7N3ktL9OwZJ81fhJTkRfAmDJcc1nuzdQJJcPnwa1IyBnbhISE5lxcDvf6b8zfF5Hatd5gH4DWmKzebF44XZm/hiWIk+/AS15OKis0U+PWJjcT0/qTtZs3mY/vZ8fphr99APlCjJgUDA5c2bjzJlLjHxvHleuXL8lffcl99C7d8XTsIi4ngAAIABJREFUd59gs3nx8qBghr4+g95dpvBI8yqULO3vlOfhoLIUK56fnh2/YPyoP3hlcDAAN67HM/jlH+nT42v69Pia2nXLUqlKMQCOHj7He0Nns2v7f/+XttdeasHg9+bQvf9UmjWqRKkSBZzyREXHMvGrlfzy25YU5Zes3M3g4XNu+fxJGvq/8ShvDfiJFzp+ziMtq1KyjHP9BNYrR7GS+en1xGdMGLGAl0Pb2PctW7idYa/8mOK4Rw+d4/03ZrFr27Gb1hTYtDJFywTwfIMPmPjmTEI+7ug233NDOzDv69X0bvgh0Zeu0KpTEABzp/xJSKtPCGn1Cd+NXMiuTQeJvniFUqoIrTsH8WrbT+nXchQPN69C0TIBGdPUsAJFS/nzXPBYJgyfR8g77d3me35gK377YQPPPzqO6KhYWj3xEABnTl5gcM+v6fvEZ/w8ZTUDhj/mVO6xbvU4fjg8o1VEYGNF0dL+PN9iNBPf/pWQ9x53m++5QY8y77v19G45muhLV2n1VKB9n83mRa9BwWxdfyBFudDuXxHSYcItGXyBjUxtLccY2ly+a7K2YENbqzFER12l1VO1M6QtQxqaVDI0PDKCiUNmE/LhU+41hLZl3rdr6N30Y6N+OtYBILdPDkI+eJL3XviWPq0+4aP+39vLLJ+7hbd6fpW+hgblKVqyAM+1n8CED+YTMqyd23zPv9qS337cyPPtJxht5vFaGSr/5gvT6P/MZCeDr3rtMgQ1qUjfpz/npScnpasRwOblxfuBLem5ahYtF35F+9KVKedbIEW+LeEnaLN4Km0WT+Wz3caDYVxCAh9tXUmLhV/zxNIf6F7hIbdl09Vg8+Llga0ZOugXej/7pft+um5ZipXIT89Okxk/+g9eGdQaMPvpAT/Sp+c39On5DbXrPkClKkUBGPhmG76dsooXe3zNhrWap7sE3bS2+5nExITb/mcV96zRp5SKtvDcTZRS9W5XvtuiqXJRTp2I5Mypi8TFJbB6xR7qNazglCeooWLFEsPLsW/PSfLkyUH+AnkAiL16A4AsWWxkyWIj0Yxx+O/YeU78F/l/aatUvggnT1/g9NlLxMUlsHLdfhrUKeeU5+KlK+w/eMbtU/uOPSeIio79vzSoKsU4dTySMyfN+lm+h6DGFZ3yBDWuyIpFOwHYv/skuX2S62f3tv+4HHU1xXGPH43gxLHzt6SpbstqrJxjeDz3bz1KHt+c5CvomyJfjfrlWbdoOwArZm8mqFW1FHkaP1aLNb//A0CJcoXYv+0Y12JvkBCfwK5NB6nXunqGNAU1rcTK+dsMTTuPk8cnB/n9fVJqqvMA65btMTT9vpV6zSoDsG/7f0RHxZrl/8O/kJ+9jH8hXwIbKZbMzbiHpG6zKqz8zfhe+3f8Rx6fnOQLcKMnqCzrzLa94rd/CGpexb6vfbf6bFi2m4vnb2+XUbdZZVbO22pqO25cP3fa6pZlnenBW/HbVoKaOWqrx4alu7h4PubWNLSoyspfjfrcv/1Y6hqCyrFusdG2V8zdQlDLqgA06VCLDUt3EX7qIgCXHOpo9+bDXL54JV0NQU0qsnKh0T737zphtpk8KTUElmHdir2GhgXbqfdIpZsq70jbjoHMmraOGw4e0/SoUaAoxy5f4Hj0RW4kJLDg2D5alKiQfkEgPDaGPRfOAhATd52DlyIonCtlPaeHquTaT++lXgPXfroCK5YY12rfnlOp99Pe3vZQtOIlC7DTfDDfuuUwDRurm9Ym3Bvcs0afxTQBMmLMZTTf/41/gA/hZ6PsnyPCL+Pv0vn7B/hwzilPlD2PzebFlO96M3vRQLZuOcJ+N8NUt6ytQB7ORVy2fw6PuExAgbQ79dtNAdf6ORvltn7Cz15KznMuigIFb75jz7Cmwn5EmD+2ABGnL+Jf2M8pj2++3MREXSXBNIYjTl+kgEue7DmyUrtJJdb/sQOAY/o0VeuUxSdvLrLnyEpg08oEFM2bMU0FfQk/k1wH4WejKFDI2RD1zZuLmMuxdk3hZ6Mo4MZYbfVEbf5el+zBeim0Dd9+uoTEmxhKKVDIlwgHPRFnL+HvqidfLuc6OnPJrrlAIV/qtajCHzOch/HAiN3+aGpvJv76MsHPPJxhTc7aHK7fmZvUVtCXes2r8Mcvf930uZ00nM5IG4p10WDkKV6mIHn8cjJqRj8mzn+NZk/U5mZx22YKptdmLtnvrbTKJybCiMnd+eznPgQ/+ZA9T7FSBahSqxTjp7/IJ988lyGdhXPm4fSV5D7gzJXLFM6Z8v6u5V+MPx59jmmPdKS8n3+K/cVy+1E5fyG2R9x8H+kf4EP4ueS+0LEPtufx9+HcOYe+6lwU/v4O/fS03sxe8Bpb/z5s76ePHg4nyDQeGz1SiYBCKe9HIQ0SEm//n0Xc8zF9Sikv4BMgGEgEPtRaz1RK5QF+B/IBWYG3tNa/K6VKA4uB9RgG2Umgg9Y6pRvHOP4rQB8gDtgLhJqf45VSzwIvA3mBt4BswHmgK5DTTb7ngYVa6znmsaO11nmUUkWAmYAvxjXrq7VedzP14IVXim2uE5K8Umaxe/QSEhLp0/MbcufJzvCPn6b0AwEcvYlhuDS1uT3vbTn0TWhIv37cVOEd1ZkRTW7zuHyu06Iqe7ccIdr0yhw/eJbZX6xgxIz+XI25xuG9J4mPy9hwg3tNiS55UpZzzVP94TK0euIhXu9mDA8+3FhxMTKGg3tPUT2wTIa0pH4ulzxu276R6aWh7Zg6ejEJbjrh1zt/QeS5y/jlz82I73pz/FA4u/8+chPaMnD90rgvXxrWlqlj3Gu7rRrSqENbFhvlq5YgtOtksufIyti5r7B/2zFOHsn4vX/rbSb98gN7fkNk+GX88uXm4yk9OH4kgt1bj+HtbcPHJyevdvuKClWLMfHHl25Np8vdtCfyDA3mfc6VuBs0KVqWLxs9SdMFycPKubJkZXLDx/ngnxVEx918zFxG7p20dCYkJNKnl9lPj3iK0mUCOHoknE8/Xkj/V1vybK8GhK3/1ylmVLi/uOeNPuAJoCZQA/AHtiil1gLhwONa6yillD+wSSk13yxTHuistX5BKTULeBJIGbBlEAqU0VpfU0rl1VpfVEpNAaK11mMAlFL5gLpa60SlVG/gDa31627yPZ/KOboAS7XWHymlvIFcN1sJ4eFRTk93/gE+nHfwrgGEn7tMwUK+7LHn8eV8hPOQV0z0NXZsO0btOmVvm9EXHhFNQYchwgB/HyIiM3d0PuKcS/0U8k1RPxHnLhNQyA84buQp6EtkuHOe/5e2PRrS2oy3ObDjP/wdPHD+RfJy3sHTCHApMprcvjmxedtIiE/Av0heIs8452ncoRarzaHdJJb9sollvxjerR5vtnXyBrnSrnMdWpsxcAd2nyDAwVMUUMiXyHPOdXDpwhVy++Swawoo5FxPZSoU4tX3HuftPt9z+ZLxLFXlwVLUbVKRhxtWIGv2LOTKnZ03Rj7NJ6GzU9ZR1yBadzQ8bwd2nXDyXPkX8uO8gxfE0BPjXEeF/eyay1ctTui4zoDh8QpsXJH4+HjCVuy157kUGcPG5XtQ1Uuka/S17VLXRVtewIjn9C+cUW1RydrGdjG15SKwsSI+LoGwlXvT1tCtPq071TU07DyOf5H02lAMuX1zuGgw8kScvkhUZAzXrl7n2tXr7N58mDKVit6U0Xc+PCplmwlPr8342fNEnL2Uavmk/5cuxLBx1T5U1eLs3nqMiLNRbPjTqKcDu1NOXHHH6SuXKZIruQ8onMuHs1ed+yFHQ271qUN8ENiSfNlzcuHaVbJ42Zjc8Al+P7qHpcdvLQYz/NxlAhxGD9z1weHhURQs6NBPF0ytn/6P2nUf4OiRcI7/d57QgTMAKFYiP3WCnMNnhHSQJVvuKhoAM7TW8Vrrs8AaIBDDbzNCKbUTWAEUAwqZZY5orbeb6X+A0mkcfyfwk+mti0slT3FgqVJqFzAYqJJKvtTYAvRSSg0Hqmmtb9rS0PtOUax4fgoXyUuWLDaaNK9CmEtweNj6AzRvbcSDVapSjJiYWCLPR+OXNxe582QHIFu2LNSqXYbjxyJuVkKq7P/3NMWL5qNIIT+yZLHRrGFFNvx18LYdPyPovScpVrIAhYqa9dOiCpvWaqc8m9ZqmrcxYt8qVi3GlehrRN7mOLCF36+zT74IW7KTZk8ZBkTFWqWJuRzLBRejAWDnxn9p2KYmAM2ffpiwZbvs+3L55KBa3XKELd3lVMbPHD4PKJqP+sE17PF+7lgw4y/6PzmJ/k9OImzlPpq1f9DQVL0EMdHXiIxI2Rx3bj5Mw5ZGM2/eoRZhf+4zzlfEj7cndGX0kDmcdIh1nDZ+Gd2afUKPlmMYOWgmO/467NbgA1j4UxghHSYQ0mECYSv20OxxY1ivYo2SxETHcsGNIb5z0yEamm27+eMPEbbS+Mns1WwUPZsaf+uX7uLz4fMIW7GX7DmzkjN3NgCy58xKrfoVOPrvmRTHTaHt502EPDaRkMcmGtoeq2VqK2FcP3fa/jpEw1ZVTW21CDONlV7NPqFns1H0bDaK9Ut38/l789I1+AAWTt9ASJtPCWnzKWHLdtmHZCvWLJW6hk0HaRhstO3mTwYSttyIMdy0fDdVA8tg87aRPUdWVM2SHD94Nl0NjoSt2k+ztkb7rFitODHRsURGpLxvdv59hIbNjdjP5u1qErbaaDOb1mi35bPnyErOXOY1ypGVWkFlOWpq27hqHzUCHwCgWMmMTajYef4UpX3yUTy3H1ltNtqVqsSKE/865fHPkduerlGgCF5eXly4Zjy4jKr7KAejzvPt/pSTzTKK3n+KYiXyU7iIn9lPVyZsg2s//S/NzRjcSlWKGveg2366NMfNeyxvXsNP4OUFXXvUZ+HvW29Z431JQsLt/7OI+8HT58ZhDhhDrAHAQ1rrG0qpo0AOc981h3zxGEOxqdEGaAS0B95WSrkz6D4Dxmqt5yulmgDDUzlWHKYhbg5LZwPQWq9VSjUyzzVdKTVaa/1DGppSkBCfyKSxS/h4XGds3jaWLtzOsSMRtDV/lBbO28rmjQepE1SO72f351rsDcZ8tACA/AXy8Mbb7bHZvPCyebF25T7+2mgYZfUbKfoPbIVf3lx8OOYZDv17liGvzbgZacQnJDL+yxWMGf4UNpuNP1bs4ujx87RvXQOA+Ut2kD9vbr4a243cubKRkJDIU+0fonv/qVy5ep13BrXlwaol8PPNyZypfZg2YwOLlu9K56wp6+fzT/5gxMRnsXl7sWz+do4dDqeNOet00a//sHnDvwTWL8+03142lo54/3d7+dAPn6D6Q6Xxy5uLHxe+xvSvVrN0/jbqNalIv0HB+OXLxQfjunDowBmGvfJThjRt+XMvgU2rMHX9O8TGXmfcwORy7//wEuMHzyDybBRTR8wn9IuedH+jDYd2n7B78ADqta7O1jX7uXbVeajpra+exzdfbuLi4vli2GyiL7mNXkjB5rWawEYVmLp4INdibzD2rV+TNU3uzvh3fiMy/DLfjl3KkDGd6PFKCw7tO8VSc3JG1z5N8fHLRcjbxqzf+LgEXnnmiwyd220drd5PYGPF1BVvEHv1OuOGJBuK73/di/HD5hB57jJTxywmdFwXur/akkN7T7Fsdto/zPn8fXj7824AeHt7s3rBNv5Zd3Pemy1rNIGNKzJ1+WBir95g3FAHbV/1ZPxbcw1to5cQOq6zoW1f+tpuSsOqfQQ+Uompq4caGt5Ivjffn/oC40NnEnkuiqkjFxL6WXe6v/4oh/aeYNksI47w+KFz/L1WM3nxIBISElk68y+OHTCM3zcnPEv1uuXwzZeb6RvfYfr4pfZyjmxed4DABuWZuuBVo828+1uyhknPMv693402M345Q0Y9TY/+zTikT7P0t61pls9XIA/vjDW8tN5ZbKxavJN/zH5p2bxtDHzvMabM6Z/hocz4xETe/Xs5PzTthM3Li9mHdvLvpQi6lDcecn7+dxuPlqxI1/IPEp+YQGx8HK+sN/qA2gHFeeKBauy/cI5FwUYM4egda1h96lAGr5SB0U8v5eOxnbHZbCxdtMPopzuY/fTvW9kcdpA6QWX5fmY/o58esRAw++lh7ZL76T+T++lHWlShvdmXrV+jWbpox03pEu4dvFzjBe4VHOLhngBeAh4F8gN/A3WAZ4ByWuuXlVKPAH8CScFEC7XWVc3jDALyaK2HuzmHDSiptT6qlMoKnAAURmyer9b6XTPfNqC31vofpdQ0jOHgJkqp113yvQX4aK3fVEo9BvymtfZSSpUCTmqt45RSrwKltdavpvH1EwFa1PvwFmvv9rF841v2dKP2aa/llRmsnT/Ynm4V+J6FSgyWbnnXng4u/oqFSpJZfGKiPd26yjALlRgs2fORPR1c4U0LlRgsPjDKng5WoRYqMVisR9rTwWUGWqgkmcVHxtrTrWu+Y6ESgyXb37eny/z0sYVKDI50HWJPt2jwURo5M4fl662/z1MhNadNptLat9dtN5SWRE2z5LvdD8O7v2EMwe7AMOze0FqfAX4Caiul/sbw+u2/hWN7Az+aw7bbgHFa64vAAuBxpdR2pVRDDM/ebKXUOsBxXNQ139dAY6XUZgzDNGmdhibAdtN4fBKYcAtaBUEQBEG4j7lnh3e11nnM/4kYcXSDXfZHAKmtUFnVId+YNM5xAyNm0HX7AcB14bPfM5ivrkN6iJnve+B7BEEQBEHIVBItjMG73dyzRp8gCIIgCML/zT0UBidGXwZRSn0O1HfZPEFrPc0KPYIgCIIgCDeDGH0ZRGvd32oNgiAIgiBkMha+QeN2cz9M5BAEQRAEQbjvEU+fIAiCIAhCaiTeOxM5xNMnCIIgCIJwHyCePkEQBEEQhFRIvIdi+sToEwRBEARBSA0Z3hUEQRAEQRDuJsTTJwiCIAiCkAr30vCuV+I9tNK0YEcuqiAIgnC342W1AIAW3s/c9t/U5fEzLflu4ukTBEEQBEFIjXsopk88fYIgCIIgCPcBMpFDEARBEAThPkCMPkEQBEEQhPsAMfoEQRAEQRDuA8ToEwRBEARBuA8Qo08QBEEQBOE+QIw+QRAEQRCE+wAx+gRBEARBEO4DxOgTBEEQBEG4D5A3cgh2lFLewCta63EeoOWJtPZrrX/NLC2OKKWeBpZorS8rpd4CagEfaq23WqHHk1BK1Uprv9V1pJTKCZTUWmsrdSShlGoAlNdaT1NKBQB5tNZHLNIyQGs9Ib1tmawpL9AdKI3Db5XW+hULtIwBpmmt92T2ue8WlFL1ge1a6xil1LMYfeMErfUxi6UJDojRJ9jRWscrpToAlht9QDvzf0GgHvCn+fkRYDVgidEHvK21nm3+YLcCxgCTgToW6UEpVQEYDJTC+cexaSZL+dT8nwOoDezAeHdmdeAvoEEm67GjlGqHca2yAWWUUjWB97XW7S3S8y5GHSlgGpAV+BGob4UeoAfgauD1dLMtM/kD2ATsAqx+D9Z+4CulVBaM6zVDa33JKjHmQ8KbQGWM+w2w5J53ZDJQQylVA3gD+Bb4AWhsoSbBBTH6BFc2KKUmATOBmKSNme2l0Vr3AlBKLQQqa61Pm5+LAJ9nphYX4s3/bYDJWuvflVLDLdQDMBuYAnxNsr5MR2v9CIBS6hfgRa31LvNzVWCQVbpMhgMPYzwwoLXerpQqbaGex4EHga2mnlNKKZ/MFqGU6gx0wTCE5zvs8gHOZ7YeF3JorQdarAEArfU3wDdKKQX0AnYqpTYAX2utV1kg6SeMProN0AfDaA+3QIcjcVrrRNNxMEFr/a1SqofFmgQXxOgTXKln/n/fYVsiYNUTZOkkg8/kLFDBIi0AJ5VSXwLNgVFKqexYHxsbp7WebLEGRyomGXwAWuvdpmfNSuK01peM32yP4Lr5A5kIoJTKbZGOjcBpwJ9kTy3AZWCnJYqSma6UegFYCFxL2qi1jrRCjBn+UtH8i8DwZA9USr2kte6UyXIKmEbVAK31GmCNUmpNJmtw5bJSagjwLNDIrK+sFmsSXBCjT3AiyVvjQaxWSi0FZmAYn50AK56sk+gItAbGaK0vmp7HwRbqAViglOoH/IYH/DgC+5RS32AMVyZi/Ajss0hLEruVUl0Ab6VUeeAVDIPHKmaZDw95TcPmOQxPbaZixlsdA4Iy+9wZ4DowGhiG0Y4w/z+Q2UKUUmMxQk7+BEZorTebu0YppayIEb1h/j+tlGoDnAKKW6DDkWcwvMbPa63PKKVKYlw/wYPwSkxMTD+XcN+glCoEjACKaq2DlVKVgSCt9bcWanocaGR+XKu1/s1CLdO11t3S25bJmtwF/ydqrTP9xxFAKZUD6IvDNcMYCo+1Qo+pKReG8dASI85wKfCBxZpaOOrRWi+3UMsTwCiMGFov8y9Ra+1roaZDQB2tdYRVGkwdXsBbwKda6ytu9vtldnyfUqotsA4oAXwG+ALvaa3np1lQuO8Ro09wQim1GCNQeZjWuoYZuLxNa13NQk2lMGY5rjB/vL211pct0rJVa13L4bM3sEtrXdkKPZ6Kp82U9VSUUr44T76xaujyINBOa221R9aOGWPYyZ2hZYGWf7TWD1mtw5NRSl0m2SObDWNoN1pr7WedKsEVGd4VXPHXWs8yYzPQWscppSybHGAOfb0I5AfKAsUwJi00y2QdQ4ChQE6lVJS52QtjCOqrzNTiilIqK86etdXAl1rrG6kWurN62mMM63jETFlTU22M61caZyOrukV6XsKIm72KMTPVC4uGLk3OepLBZxIPbFdKrcI5bCHTl2wBNimlArXWWyw4dwrMGfuTgUJa66pKqepAe631h1Zp0lo7TURSSj2GMXlK8CDE6BNciVFKFcB8YlNK1QUsW5oA6I/RcfwFoLX+VylVMLNFaK0/Bj5WSn2stR6S2edPh8kYT9VfmJ+7mdt6W6TnXTxrpiwYsx0H4xnLf4Axm7mK1UOXDvytlJoJzMPZwLJqaSRMLfMsPL8jjwAvKaWOYaxqkDT8bclDA0b852DgSwCt9U6l1M+AZUafK1rreUqpUKt1CM6I0Se4MhCYD5Q1lyQIAJ6yUM81rfX1pFmX5nCzZTEJWushSqlipFwTb61VmoBArXUNh89/KqV2WKbG82bKAoR7WLzTIcDyYUsHfDH0tHTYlohF62GaYRPdtNbNrTi/G4KtFuBCLq31Zpd7LM4qMZBiQX0bxjqUEj/mYYjRJzihtd6qlGqMsWisl7HJmmFCkzVKqaRh1RZAP2CBVWKUUiMxZhDvJXlNvESMyQpWEa+UKqu1PgSglHoAC9frw/NmygK8a84oXolneLKGABuVUn9h/dClfV1MT8FcKP6KFZMkUtFzzFx0uKG5aZ3W2soHqwilVFmSR2Sewlh6x0raOaTjgKNAB2ukCKkhRp8ApPnaswpKKSt/HEOB5zGG5V7CWKX/G4u0gLGortJaX0s3Z+YxGFillDqMYaiXwlhA1ipexpgpew34GWOmrNXDTr0w1lfLSvLwrmWeLIxhuT/xkOFmpdQ03HhltNbPWSAniVhgl1JqOc4LxVvxGrYBwAskt5cflVJfaa0/y2wtJv0xYokrKqVOAkeArhZpSeIbrfUGxw3KeDXbOYv0CG4Qo09IwiNfe6a1TsCIX8n0NcxS4TCG4eAxRp/WeqXpUUvyzu632Ch9QGs9DMPw8xRqWDkD3Q1xnvK2CZOFDukcGA83pyzSksQi888TeB5j+ZgYAKXUKCAMY7mUTMUc+u6rtW5uLupts2o1Axc+w3jfbnrbBAsRo08APO+1Z0qpWVrrjkqpXbj3QFgVQH0FY0ah6zChFd6HplrrP914acta7J2dopTKBnwH/Ky1vmiRDkc2KaUqa633Wi3EZJVS6kWMUAXLF9TWWs91/KyUmgGssEJLElrr7z1o6R8vnEMm4s1tmY459P2QmY5JL/+dRikVhOEoCFBKOT7I+ALe1qgSUkOMPsEVT3nt2QDzf1sLzp0W880/T6Axhke2nZt9lg1daq0bmEtK9MKYFboZmGbl4sNAA6CHuZD1NayffdnF/O84E9zKJVtcKQ+UtFKAUqodMAbPWPpnGvCXUippYfjHgKkW6Ehim7mO4Wych76tuOezAXkw7AnHZVuisHYSoOAGWZxZcEIpNQmjw3d87dlBrfXLFmjxxnhTgafM4AM8b+FhpVQZrfWR9LZlNub1ewyYiPED4AUMteKHyVzgOwXma8juexwW1k1aL/AMMMTVA5jJmv7BeOf3aq31g+a2XVYN0yulamE8PHhhvBlomxU6TC3T3GxOtDIGUylVSu4nz0c8fYITWusQc7gwaZbaV1a99szTZvCBx3kfkphLyriZOYAlbxAwF4rtBbQBlmO86WGrUqooRhxUpht9njb70tMW1HZdWNdDcLf0jyVeCpX8qsWtbrZlOp4229rkilJqNFAFIy4UAK11U+skCa6I0SekwPTEWLkoqyMeM4PPZDgpFx4uY4UQpVRFjA7WzyWuzxeHTtcCJmFMvBmqtb6atFFrfUop9ZYVgjxw9qWnLaid9CYVuxGqtV6YVv5MwJOW/qni+MH0Ylv2WjZlvN/6eVIaWFbOtv4JmIkRktMH6AGEW6hHcIMYfYITyvNevO5JM/jAg7wPGLN12wJ5cY7ru4xh4FiC1rqROZGjgllP9rUetdbTLZLlMbMvTTxqQW1z/clAjB9ugAFKqfoWv33G3dI/H2SmAOW5r1+cDuwHWmG8zq8rYPVr9Aporb9VSg3QWq/BWGN1jcWaBBfE6BNc+QQPefG6B67KDx7kfdBa/w78rpQK0lqHWaHBHebi3j9gLM7qBZRQSvWw+K0lHjP7Mun8Hrag9qNATXOJJJRS3wPbcJ5oktm0cV36Ryn1NMbkhUzBg1+/WE5r/bRSqoM5yznJKLaSpNCE00qpNhhL/hS3UI/gBjH6BFc85sXrnhjTh7P3YQYWeB/csE0p1R/PGep0S5keAAAZjUlEQVQZC7RMmuhizuSdgYXDYbifffmthXo8bUFtMDzGSUvG+FkpxGQIKQ08d9syg4VKqdxa6xil1LMYMbQTLJy4kGRgXVRKVcWYeFPaIi1JfKiU8gNex/Cg+wKvWitJcEWMPsEVT3vxukfF9Gmtr2AYfZ608LCnDfVkdZzZrLU+YE5csAyt9Vil1GqSZ1/2snL2pQcuqP0xxsPDKlPP/9q79yi7y+r+4+8Ey70RgqkWqUDVflxRoaFQEJFf8VJFC+FuKVYEWqUoBv2VooiCxCsIrdAKBuRSSEEuSwNIUdEgNzUSEFDK56eiUpEYQcAUQW75/fE8J3NyMplkbPLdz8zs11pZJ9/vZNbZa2ZyZp/nefbeuxK0yidpd8rK4/Mlndb3oSnEzZc9A9i2FgP9E+UNw79T2iZFmCNpU+A4SgupjYEPBsXS81B9c/4Ipal/byJHakgmfWlQU4PXaeRMn6QrGeHsXnD1bmtbPbdI+hwlGYWShC4MjAdJOwHft31rvf59STva/nZQPO8E5tq+o15vKukw259ZxaeuFbYvqknxDpSk7xjbiyJioWwL3gLsyfI/N0uA94REVM7yLpU0k7LC9zlJBwfFgu3eKMrrGaa3Yz1OcX63UeVEjrEgk760nNZaAQS8cK3Mp+rjPsDzgAvr9YGUs2uRWtvq+QfKbNB3U3uaMVSlGuUMlv/l8+gw97r097aXTbqx/ZCkvyf26zStPq4D7Bw11aW20rld0n+M1MJG0uW29+0orCW1qOMtwK71vHHo6vUqzAI6ee3MiRxjSyZ9aTn1/NUZwHNtv6z2XNvT9keC4nkxZetpOsufV+t0ckGtRkPSbNu79n3oSkmRBQow/FbPh6KCqduUp9Y/rZhke9lKre1nJEW+/k2WtCymmkSsGxWMpHOAbYDvA8/U25Er/KxGz8IuXwPeTJmicpjtRZJeAJzc4fOPVpdFSjmRYwzJpC8NOotyyPyzALbvqNuFIUkf5QD+8cA/U86JHEJs1eU0SX9s+x4oky8YWiEJsaqtnq5oJXOSewJHngHcI+ndlDc0AEcA9wTG82XgEklnUr5mhwPXBMazk+3pgc//u+isVVLd6j617/peypm+VnX5tem1ZzmvV9giaTKwse1fj/zZqWuZ9KVBG9peMNCHLurwNMAG9dD7pPqCcoKkGyiJYIT3ANfVqkso26jvCIoFAEkfA06y/XC93hT4v7a7boTc2pzkfodTxsEdR/mF+DXg7YHxHEP5ufkHypuYrwBnj/gZa9c3JU23fVdgDM2RdKPLLOnemLqe6P6lqxLxxvjjkg6ntB5aSGkaf6rtlldEJ5xM+tKgByS9kPoCJ2k/4P7AeB6v7xp/IOldwH2UxtEhbF9Tt5xfUm9FV10C7G772N5FPR/2RkqC05n+9hWSnkeZXLIU+E5gUQAAthdT5kg3ofbDO4Ohlcdo51MSv0WUqv1eUhO5Orsqaz2xsb1LfWxqTJ2kdWyP1Nfxps6CGTLd9q8lHQRcTXljs5C2t8EnnEz60qB3UjrNv0TSfcCPKdWXUY4CNqQUBcymbPG+NTAeKP3mtqL8/9m2HniP3OpZR9J6veRT0gbAelHBSPo7ypnCr1N+MZ8u6UTb5wTGNI0ypWQr+l73onoZ1lYWJ1D68z2LoSQranv+HMoouDsZOtMXqk52+PQI947pOJ51gOey/M/PvV3G0OeHki4Dzh1uddb2uwJi+r3ammkv4F9tPykpalpRWolM+tKgvSjv0uYDkylVjq+VtND2dwPiWUpp/bElQ9VyZ1EOnXdO0gXAC4HvMjRBYSmx53suBL4m6dway6F0VLm3EkcDM2w/CCBpM8rUkrCkD5gH3ABcS+zki57PUY4KLKSNeO61fUV0EAMOBj49cO9tvXu2v9JVIJKOpBwp+QXLF7pErYRuQ1m5PrvuhJwDXBx8hu6zlE4GtwPXS9qSUsyRGpJJXxq0ff1zBWX14SDgO8Dhki61fVLH8cylJBGtrEBsT9nGaOYdrO2TahHFayjfs9m2I/v0/YzSU61nCfDfQbH0bGi705WhVXjE9n9GB9Hn7lqwdSXBTdklHUiplN1aUn8i+vvAg13HU80C1HsjE832Esqb37Mk7UqZePPPdfVvtu0fBsR0GuXcLACS7qU2aa7XEb0D04BM+tKgzYDtbP8PgKTjgcsoHfoXUmbzdumXja1AfI/Spy/ynOMKagLRShJxH2Xk2TzKashMYEGvh5ftiFYuV0l6o+2rA557OPMlnUxpidKfZN0aFM8GNY4WmrLfTPn/9RzglL77S4A7AuKB8qallVGQva3mN1G6GWxF+TrNBV5F2an5k7DgqvrGuL8IsLPegWnlMulLg14APNF3/SSwpe3HJEUULBwv6WxKtWULY+GeA9wlacFAPGETOQYqC9elbIM/GlhZ+KP6p2defYw8DD8LOLb+DD9JfPXljvVx+757S4FXB8TSVFP2WhD0U+AV0bH0NRu+h1K1/yWW/38f1YvyB5QjOCfbvrnv/mV15a9Fka22UpVJXxr0H8C36ioNwB7ARZI2AiLaORxCqZT9PdpoGntC0POu1GBloaS9KJWzIWx/OOq5V6a16kvbu636X3VH0vrAYcBLWb4JekihS41pH+CTlGr9ScQk6r2fm3vrn3UJbKLd5622b+y/IemVtm+Kmku+Gpo5EjORZdKXlmN7tqSrGRpMf7jtW+qHI6p4t7X98oDnHZbtb9QDyi+2fa2kDWls1JDtL0p6X9TzS9oe+ABDlam9uDo/9C7pJbbvljTsuLWut1MlvcX2hQPjqvrjiVo5ugC4G3g9cCLl//p/BcXScxKwh+2wOFb3DYyk020fubbj6XMaY2/Oba70NSCTvrQC2wtZftB5pG+11DS2zkd9OzCVUsX7fOBMShFFVEz79F1OpmwZRr6rbqn45r2U79cpw3wsYjt1o/rY1Moj8CLb+0uaafv8WtQRWQwE8IvIhG+UXtnFk4zxObcRvQPTgEz6Uut2AQ6W9GPaaBr7TsrW6bcBbP9AUliz6GqPvr8/RWmbMDMmFKCh4hvbb6+PI26nSnqd7a92EE9vvOGIK0iS3m/742s7nj69ObcPS3oZsIhSIBDpFkmfB75IG+d5W9DsnFtJ6wH7smIvzBPrY0TvwDQgk77UujdEBzDgt7af6I2pk/Qsgs+qtHQIv2qt+GZ1fBJY60nfKOwPdJn0zanj+46jtGvaGPhgh88/nCnAb2ijorgJ9XjJjcDLGzw7O49S4byQvv/3qS2Z9KWm9Y/2asQ3JB0LbCDpdcARlN5mnZN0OiMknIEHulsrvlkdrZ036jQe2725v9cDK0wFieix1uCbmZF09v2y/bSkqV093yhsYbu1N+lpQCZ9KY3O+yhVjncC76D0xDp7xM9Ye3oFNq8EpgOfr9f7E3sms6nim9XUWmVha/F03mOtb8LMciIqiiXtb/vSEe4NTg5Z226rjasvpUxNAsJX02+W9HLbdwbGkFYhk76URsH2M5LOp5zpW1puxUzn6K28SHobsJvtJ+v1mUBnI6qG0VTxzRg1oVceq6v6/r4+sDfw84A4AN5PSbCGvWf7vI7jmUqZTtJfiBS9mr4L8LaGzl+nYWTSl9IoSHoTpVr3R5QXta0lvSN4pNbmlEPdv6rXG9d7UVorvkHSerZ/O8K9n3Qczytt3zTCvcEEI1rnb2xsX95/LekiyuzkzkjaHXgj8HxJp/V9aArLT5voVKNb37tHB5BWLZO+lEbnFMqq2g8BJL0Q+BKxI9A+Adwq6bp6/X+IbSLd4rmeb7JiD7Nl92zvs8JnrF3D9VRbds/2xzqOZ1VaWHl8MWViUJd+TjlGsSfLH5lYAryn41iWkfQnwBnAc22/TNI2wJ62PxIVU+/8de1msP4q/nkKkklfSqOzeGCY+T3A4qhgqvOAp4GjKMnehyjzgUPY/qmkXSgNrM+VNI2y+tg5Sc+j9FLcQNIMhpKXKcCGAfGM1T5rnfdY6xsvOKk+LgKO6TIG27cDt0uaaztsZW8YZ1F6YfZaAN1ReyuGJX2S9qS8Kd6c8pq4JaXB90ujYkoryqQvpdH5fp1YcgnlF9H+wHd6DZKDDlJ/hlIlu4HtK2rrjcuBHQJiQdLxlAbRAs6lVPFeSEcNbAe8HngbsAXQP+1iCXBsQDxN9llrscdaC6PzJF1i+wBK4cRwRSVRRxY2tL2g1zqqik5KZwM7AdfaniFpN+DA4JjSgEz6Uhqd9YFfULZQAX5JOVS9B3EHqXe0vZ2k2wBsPyQpcj7o3sAM4NYaz88lhfwCr8Uu50vad/CMWFA836C0/TmvsXZETfZYq6tHu9bL62xfNdK/Xwtm1ce/6vh5V+WBerRkKYCk/YD7Y0PiSdsPSposabLt+ZI+GRxTGpBJX0qj0OgB6iclrcPQL4BpxI4/e8L20t7KiKSNVvUJHbhK0t+wkpWsAOtJmjNMPF2PhetprseapE9QVqvn1luzarHL+7uKwfb99bGlBB3KZKA5wEsk3Qf8mJjZ6P0elrQxcAMwV9Ji4lcf04BM+lIahRYPUFOGr38B+ANJH6VsEx4XGM8lkj4LbFJnFR9KOYMUqbWVrEspVeBnU85jRmuxx9obgT+1/QxAbZV0G6VVSif6zhUOy/aUrmIZeN57gNfWN1STbS+JiGPATOBxytnig4BnA1FvqtJKZNKX0ug0d4Da9lxJC4HXUA697xU5qN72p+q0kl9TzvV9qIu5tqvQ2krWU7bPiA6iT6s91jZhqBXRs7t+8t65QkknUgpJLqB8bQ5i+TOZnZK0GXA85fu2tI5mO9H2g1Ex2X5U0nMpq7MPAv8ZGU8aXiZ9KY1OiweosX03cHd0HD01yRs20ZP0Tduv6Dik1layrpR0BGWFtn8+8a9W/ilrVYs91j5OKaCYT0m0dqXDVb4Br7e9Y9/1GZK+DZwUFM/FlJF5+9brgygTeV4bFA+SDgBOBq6jfL9Ol3S07cuiYkoryqQvpdFp8QD1WBPRw6u1layD6+PRffeWMszc2y602GPN9kW19+QOlO/XMbYXBYXztKSDKMnWUkpVauS2/FTbs/uuPyJpr7Boig8AO9heDMvOFl8LZNLXkEz6UhqdFg9QjzURY+uaWsmyvXV0DP0a7rE2rT6uA+wsKaot0t9Q5ut+mvLze1O9F2W+pL+mtI6Cco73S4HxQDlb2N+z9EFgclQwaXiZ9KW0GgYa6V4NzKe8oD1K2WI5dbjPS21ocSVL0suA6fTFY/vfg8JprseapHOAbYDvM1SNHtIWyfZPKIUKrXgH8F5K/0uor0X1dWppUIHJNZK+DFxUr99Mea1MDcmkL6XV0zu0Lcp20zzKltPfUs7WTHjDzbddic5HerW2klUbWP8FJem7mrISeSMQlfS12GNtJ9vTg2MA2qvab6Fx9SDbR0val9KEfRIwx/YXgsNKAzLpS2k12P4wgKSvANv1WiRIOoHSfiPVWbaSLrD9tyP8u5E+tra0tpK1H7AtcJvtQ2rV49mB8bTYY+2bkqbbvis4Dmiwar+BxtUrqA3Qw5ugp5XLpC+l0XkB8ETf9ROUBrsJ1pV0MOXs1T6DH+ydxbL9vc4ja28l6zHbz0h6StIUyupjSBFH1WKPtfMpid8i4otvmqraX0nj6l1svy8glpX1Mux9v0J6GabhZdKX0uhcACyQ9AXKC93elF9OCQ6nJAybUMbS9YsaUdfTW8m6njZWsm6RtAllBWkh8D/AgqhgGu2xdg5lVfhOYifMQHtV+ytrXN150tfiVnNauUlLl0YU0qU0dknaDnhVvbze9m2R8bRG0mG2PxcdR786ueAxyoH33krW3AYSGyRtBUyxfUdgDIM91l4FhPZYk/T1wLF0y5H0x5Sq/Z2Bh6hV+1Hj2STdAfxFr6+jpKmULd7oZtqpcZn0pZTWiOG2dPsFtdoAQNLWwP22H6/XG1AO5f8kKJ69ga/bfqReb0L5Jf7FoHhuB1432GPN9rYR8dQYPkNZNb6S5RtYd/pzVOdaf6IWKjQx9qy2a/kkpYvAssbVti+OjCu1L7d3U0pryuCWbr/o7d1LKas0PU/XezvEhMPx/ZWNth+uFb0hSR9t9ljbgJLs/WXfvc5/jmw/LenP6t8f7fK5hyNpMmW7eyfaaFydxpBM+lJKa4TtQ6JjGMGzbC8rwLH9hKR1A+MZLqGKfD1ursdaYz9Pt0m6gvJGYVniF7F6XQuA3mX7EuCKrp8/jW2Z9KWU1qhaEPAxYHPbu0uaDrwi+JzfLyXtafuKGuNM4IHAeG6RdCrwb5TVqyMpBR0hWuyxJml94DBKL8X+BtaHBoQzlbL62X/GMHL1+quS/pEyb7c/CY2a3ZzGiEz6Ukpr2nnAuZRZnAD/j/LLKTLpO5xStfuvlKTmv4G3BsZzJPBBytdlEvAVyoi/MA32WLsAuBt4PaV9zEGUhtoRJgOzbD8MIGlTSrPvKIdSks4jBu5Htv1JY0AmfSmlNe05ti+R9H4A209JihxOj+0fATvVti2Tog/i17NhnbfXGNR4j7UX2d5f0kzb59dmyF8OimWbXsIHYPshSTOCYoEyyeUIYBfK9+8G4MzAeNIYkUlfSmlNe1TSZgz1NNsJeCQiEElvsX3hwOxkek12bXc6M1nSv9g+StKVDJNs2d6zy3ga77H2ZH18uM4pXkRcI/TJkja1/RAsa5ES+fvzfODXwGn1+sB674CwiNKYkElfSmlNey/lgPkLJd0ETKOMHYuwUX1sJbm5oD5+KjSKsWFO3UY9jvLztDFlSzzCKcDNki6jJOsHAB8NigVAA+105te2OymNKPv0pZTWKEn7U7bh/gjYF9gR+KDtW0MDS+OKpINtdzYNpxYkvZqy9f21yJnAks4DzrT9rXq9I3Cw7cEzfiktJ5O+lNIaJekO29tI2oVSxXsKcKztHQNiOW2kj9t+d1exAEi6k5HP0OVEhdUk6Vbb20XHEUHSfwEC7q23XkApcnmG/DlKI8jt3ZTSmtYr2ngTZTVinqQTgmIJa4OyEn8VHcA4Mik6gEBviA4gjU250pdSWqMkXQXcB7wW+DPKzNsFkSO9eiRNoayEhFbv1lieB/w5ZeXvOzlRYXQm8kpfSr+r6DE7KaXx5wDKmb431DYXU4GjIwOStH3dWr0D+J6k23ujtYLi+TtgAbAPpcjlW5Iimg6PZRN5pS+l30lu76aU1ijbv6FvUoHt+4H74yIC4BzgCNs3ANTzhucCUWefjgZm2H6wxrMZcHONM62em6IDSGmsyaQvpTQRLOklfAC2b6yNiaP8DOh//iWUKSGpkrQepfp7K/p+V9k+sT6+KyaylMauTPpSShPBAkmfBS6inKF7M3CdpO0AAtrJ3Ad8W9K8Gs/MGuN7azydNo1u1DxKU++FwG+DY0lpXMikL6U0EfxpfTx+4P7OlKTr1d2Gw4/qn5559bGVJtIt2MJ2VqmmtAZl9W5KKQVpqZq4NZLmAKfbvjM6lpTGi0z6UkrjXi2UOJ6hAfU3Aif2CikC4tmeUkjSW9l7BDjUdmt9BcNIugt4EfBjyvZuNrBO6X8pt3dTShPBxcD1lMIAgIOAz1N6CUZorZq4RbtHB5DSeJNJX0ppIphqe3bf9Uck7RUWTXvVxM2x/VMASX8ArB8cTkrjQiZ9KaWJYL6kvwYuqdf7AV8KjKe1auLmSNqTMrd5c2AxsCVlvuxLI+NKaSzLM30ppXGvrqJtxNBc4HWAR+vfl9qe0nE880f48FLbXVcTN0fS7ZSq6mttz5C0G3Cg7bcHh5bSmJVJX0ppQpA0FXgxfVuFtr8RF1EaiaRbbG9fk78Ztp+RtMD2n0fHltJYldu7KaVxr866nQVsAXwX2Iky9uw1gTG9ibJV2Z+EnhgVT4MelrQxcAMwV9Ji4KngmFIa0yZHB5BSSh2YBewA/NT2bsAM4IGoYCSdSTnHdySlFcn+lDNrachM4DHgKOAaSjPrPUIjSmmMy6QvpTQRPG77cSgzXW3fDSgwnp1tvxV4yPaHgVcAfxQYT3NsPwo8B3gD8CBwcVRfxZTGi0z6UkoTwc8kbQJ8EfhqnXn788B4HquPv5G0OfAksHVgPM2RdACwgLIKegBlVvF+sVGlNLblmb6U0rhne+/61xNq5eyzKVuGUa6qSejJwK2Uti1nBcbTog8AO9heDCBpGnAtcFloVCmNYVm9m1JKgSStB6xv+5HoWFoi6U7bL++7ngzc3n8vpTQ6udKXUkqxTs/ec8O6RtKXKQ2soRS+XB0YT0pjXp7pSymlWNtHB9Ai20cDcyjziLcF5tg+JjaqlMa2XOlLKaVYi6MDaJXty4HLo+NIabzIM30ppZSaUUfmDfeLaRIBI/NSGk8y6UsppY5J2gI4HdgFeAa4EZhl+2ehgaWUxrU805dSSt07F7gC+EPg+cCV9V5KKa01eaYvpZS6N812f5J3nqSjwqJJKU0ImfSllFL3HpD0FobakRxIGTWWUkprTW7vppRS9w6ljBZbBNwP7AccEhpRSmncy5W+lFLq3mzgYNsPAUiaCnyKkgymlNJakSt9KaXUvW16CR+A7V8BMwLjSSlNAJn0pZRS9yZL2rR3UVf6cuclpbRW5YtMSil17xTgZkmXURoRHwB8NDaklNJ4l82ZU0opgKTpwKspkya+Zvuu4JBSSuNcJn0ppZRSShNAnulLKaWUUpoAMulLKaWUUpoAMulLKaWUUpoAMulLKaWUUpoAMulLKaWUUpoA/j+gD453arl7mwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "corr = df.corr()\n",
    "mask = np.zeros_like(corr)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "with sns.axes_style(\"white\"):\n",
    "    ax = sns.heatmap(corr, mask=mask, square=True,annot=True,linewidths=2, cmap='viridis')\n",
    "plt.title('Correlation Matrix for Loan Status')"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.03998,
     "end_time": "2020-09-08T03:16:08.627145",
     "exception": false,
     "start_time": "2020-09-08T03:16:08.587165",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "From the above figure, we can see that Credit_History (Independent Variable) has the maximum correlation with Loan_Status (Dependent Variable). Which denotes that the Loan_Status is heavily dependent on the Credit_History."
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.033935,
     "end_time": "2020-09-08T03:16:08.695352",
     "exception": false,
     "start_time": "2020-09-08T03:16:08.661417",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4-Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.032031,
     "end_time": "2020-09-08T03:16:08.760801",
     "exception": false,
     "start_time": "2020-09-08T03:16:08.728770",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### 4-1-LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:08.836085Z",
     "iopub.status.busy": "2020-09-08T03:16:08.834968Z",
     "iopub.status.idle": "2020-09-08T03:16:08.838649Z",
     "shell.execute_reply": "2020-09-08T03:16:08.837901Z"
    },
    "papermill": {
     "duration": 0.046169,
     "end_time": "2020-09-08T03:16:08.838802",
     "exception": false,
     "start_time": "2020-09-08T03:16:08.792633",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X=df.drop(['loan_id','loan_status'],axis=1)\n",
    "y=df['loan_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:08.914572Z",
     "iopub.status.busy": "2020-09-08T03:16:08.912890Z",
     "iopub.status.idle": "2020-09-08T03:16:08.916930Z",
     "shell.execute_reply": "2020-09-08T03:16:08.916185Z"
    },
    "papermill": {
     "duration": 0.045963,
     "end_time": "2020-09-08T03:16:08.917063",
     "exception": false,
     "start_time": "2020-09-08T03:16:08.871100",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:08.999826Z",
     "iopub.status.busy": "2020-09-08T03:16:08.998594Z",
     "iopub.status.idle": "2020-09-08T03:16:09.004496Z",
     "shell.execute_reply": "2020-09-08T03:16:09.003657Z"
    },
    "papermill": {
     "duration": 0.050464,
     "end_time": "2020-09-08T03:16:09.004682",
     "exception": false,
     "start_time": "2020-09-08T03:16:08.954218",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "lr=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:09.083738Z",
     "iopub.status.busy": "2020-09-08T03:16:09.082561Z",
     "iopub.status.idle": "2020-09-08T03:16:09.138437Z",
     "shell.execute_reply": "2020-09-08T03:16:09.139113Z"
    },
    "papermill": {
     "duration": 0.101714,
     "end_time": "2020-09-08T03:16:09.139285",
     "exception": false,
     "start_time": "2020-09-08T03:16:09.037571",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:09.218744Z",
     "iopub.status.busy": "2020-09-08T03:16:09.217862Z",
     "iopub.status.idle": "2020-09-08T03:16:09.221479Z",
     "shell.execute_reply": "2020-09-08T03:16:09.220709Z"
    },
    "papermill": {
     "duration": 0.04748,
     "end_time": "2020-09-08T03:16:09.221608",
     "exception": false,
     "start_time": "2020-09-08T03:16:09.174128",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "lr_prediction=lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:09.305929Z",
     "iopub.status.busy": "2020-09-08T03:16:09.304450Z",
     "iopub.status.idle": "2020-09-08T03:16:09.319854Z",
     "shell.execute_reply": "2020-09-08T03:16:09.321172Z"
    },
    "papermill": {
     "duration": 0.064783,
     "end_time": "2020-09-08T03:16:09.321445",
     "exception": false,
     "start_time": "2020-09-08T03:16:09.256662",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10 21]\n",
      " [ 0 78]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.32      0.49        31\n",
      "           1       0.79      1.00      0.88        78\n",
      "\n",
      "    accuracy                           0.81       109\n",
      "   macro avg       0.89      0.66      0.68       109\n",
      "weighted avg       0.85      0.81      0.77       109\n",
      "\n",
      "\n",
      "\n",
      "Logistic Regression accuracy:  0.8073394495412844\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,lr_prediction))\n",
    "print('\\n')\n",
    "print(classification_report(y_test,lr_prediction))\n",
    "print('\\n')\n",
    "print('Logistic Regression accuracy: ', accuracy_score(y_test,lr_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.036852,
     "end_time": "2020-09-08T03:16:09.395570",
     "exception": false,
     "start_time": "2020-09-08T03:16:09.358718",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4-2-Decision Tree "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:09.472762Z",
     "iopub.status.busy": "2020-09-08T03:16:09.471994Z",
     "iopub.status.idle": "2020-09-08T03:16:09.475399Z",
     "shell.execute_reply": "2020-09-08T03:16:09.476025Z"
    },
    "papermill": {
     "duration": 0.045616,
     "end_time": "2020-09-08T03:16:09.476198",
     "exception": false,
     "start_time": "2020-09-08T03:16:09.430582",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dt=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:09.554316Z",
     "iopub.status.busy": "2020-09-08T03:16:09.553517Z",
     "iopub.status.idle": "2020-09-08T03:16:09.562792Z",
     "shell.execute_reply": "2020-09-08T03:16:09.562036Z"
    },
    "papermill": {
     "duration": 0.051438,
     "end_time": "2020-09-08T03:16:09.562921",
     "exception": false,
     "start_time": "2020-09-08T03:16:09.511483",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:09.648972Z",
     "iopub.status.busy": "2020-09-08T03:16:09.647648Z",
     "iopub.status.idle": "2020-09-08T03:16:09.655685Z",
     "shell.execute_reply": "2020-09-08T03:16:09.654587Z"
    },
    "papermill": {
     "duration": 0.060434,
     "end_time": "2020-09-08T03:16:09.655936",
     "exception": false,
     "start_time": "2020-09-08T03:16:09.595502",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dt_prediction=dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:09.770198Z",
     "iopub.status.busy": "2020-09-08T03:16:09.768911Z",
     "iopub.status.idle": "2020-09-08T03:16:09.785597Z",
     "shell.execute_reply": "2020-09-08T03:16:09.786224Z"
    },
    "papermill": {
     "duration": 0.079809,
     "end_time": "2020-09-08T03:16:09.786410",
     "exception": false,
     "start_time": "2020-09-08T03:16:09.706601",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[14 17]\n",
      " [15 63]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.48      0.45      0.47        31\n",
      "           1       0.79      0.81      0.80        78\n",
      "\n",
      "    accuracy                           0.71       109\n",
      "   macro avg       0.64      0.63      0.63       109\n",
      "weighted avg       0.70      0.71      0.70       109\n",
      "\n",
      "\n",
      "\n",
      "Decision Tree Accuracy:  0.7064220183486238\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,dt_prediction))\n",
    "print('\\n')\n",
    "print(classification_report(y_test,dt_prediction))\n",
    "print('\\n')\n",
    "print('Decision Tree Accuracy: ', accuracy_score(y_test,dt_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.033503,
     "end_time": "2020-09-08T03:16:09.861581",
     "exception": false,
     "start_time": "2020-09-08T03:16:09.828078",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### 4-3-Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:09.935180Z",
     "iopub.status.busy": "2020-09-08T03:16:09.934364Z",
     "iopub.status.idle": "2020-09-08T03:16:09.938296Z",
     "shell.execute_reply": "2020-09-08T03:16:09.937543Z"
    },
    "papermill": {
     "duration": 0.043446,
     "end_time": "2020-09-08T03:16:09.938436",
     "exception": false,
     "start_time": "2020-09-08T03:16:09.894990",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rf=RandomForestClassifier(n_estimators=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:10.023125Z",
     "iopub.status.busy": "2020-09-08T03:16:10.022288Z",
     "iopub.status.idle": "2020-09-08T03:16:10.553028Z",
     "shell.execute_reply": "2020-09-08T03:16:10.552196Z"
    },
    "papermill": {
     "duration": 0.581309,
     "end_time": "2020-09-08T03:16:10.553166",
     "exception": false,
     "start_time": "2020-09-08T03:16:09.971857",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=200)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:10.722199Z",
     "iopub.status.busy": "2020-09-08T03:16:10.721311Z",
     "iopub.status.idle": "2020-09-08T03:16:10.752617Z",
     "shell.execute_reply": "2020-09-08T03:16:10.751794Z"
    },
    "papermill": {
     "duration": 0.165155,
     "end_time": "2020-09-08T03:16:10.752749",
     "exception": false,
     "start_time": "2020-09-08T03:16:10.587594",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rf_prediction=rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:10.832579Z",
     "iopub.status.busy": "2020-09-08T03:16:10.831750Z",
     "iopub.status.idle": "2020-09-08T03:16:10.847098Z",
     "shell.execute_reply": "2020-09-08T03:16:10.846243Z"
    },
    "papermill": {
     "duration": 0.060092,
     "end_time": "2020-09-08T03:16:10.847243",
     "exception": false,
     "start_time": "2020-09-08T03:16:10.787151",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[12 19]\n",
      " [ 5 73]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.39      0.50        31\n",
      "           1       0.79      0.94      0.86        78\n",
      "\n",
      "    accuracy                           0.78       109\n",
      "   macro avg       0.75      0.66      0.68       109\n",
      "weighted avg       0.77      0.78      0.76       109\n",
      "\n",
      "\n",
      "\n",
      "Random Forest Accuracy:  0.7798165137614679\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,rf_prediction))\n",
    "print('\\n')\n",
    "print(classification_report(y_test,rf_prediction))\n",
    "print('\\n')\n",
    "print('Random Forest Accuracy: ', accuracy_score(y_test,rf_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.034116,
     "end_time": "2020-09-08T03:16:10.916017",
     "exception": false,
     "start_time": "2020-09-08T03:16:10.881901",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### 4-4-KNearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:10.999870Z",
     "iopub.status.busy": "2020-09-08T03:16:10.997521Z",
     "iopub.status.idle": "2020-09-08T03:16:11.536747Z",
     "shell.execute_reply": "2020-09-08T03:16:11.537673Z"
    },
    "papermill": {
     "duration": 0.5862,
     "end_time": "2020-09-08T03:16:11.537880",
     "exception": false,
     "start_time": "2020-09-08T03:16:10.951680",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.3853211009174312, 0.48623853211009177, 0.4036697247706422, 0.44954128440366975, 0.43119266055045874, 0.45871559633027525, 0.3486238532110092, 0.3669724770642202, 0.3302752293577982, 0.3302752293577982, 0.3119266055045872, 0.3394495412844037, 0.29357798165137616, 0.30275229357798167, 0.29357798165137616, 0.3119266055045872, 0.3211009174311927, 0.29357798165137616, 0.27522935779816515, 0.27522935779816515, 0.27522935779816515, 0.27522935779816515, 0.26605504587155965, 0.26605504587155965, 0.27522935779816515, 0.27522935779816515, 0.28440366972477066, 0.27522935779816515, 0.28440366972477066, 0.28440366972477066, 0.28440366972477066, 0.28440366972477066, 0.28440366972477066, 0.28440366972477066, 0.28440366972477066, 0.28440366972477066, 0.28440366972477066, 0.28440366972477066, 0.28440366972477066]\n"
     ]
    }
   ],
   "source": [
    "error_rate=[]\n",
    "for n in range(1,40):\n",
    "    knn=KNeighborsClassifier(n_neighbors=n)\n",
    "    knn.fit(X_train, y_train)\n",
    "    knn_prediction=knn.predict(X_test)\n",
    "    error_rate.append(np.mean(knn_prediction!=y_test))\n",
    "print(error_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:11.638085Z",
     "iopub.status.busy": "2020-09-08T03:16:11.636111Z",
     "iopub.status.idle": "2020-09-08T03:16:12.063158Z",
     "shell.execute_reply": "2020-09-08T03:16:12.063821Z"
    },
    "papermill": {
     "duration": 0.486515,
     "end_time": "2020-09-08T03:16:12.064009",
     "exception": false,
     "start_time": "2020-09-08T03:16:11.577494",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Elbow Method')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.set_style('whitegrid')\n",
    "plt.plot(list(range(1,40)),error_rate,color='b', marker='o', linewidth=2, markersize=12, markerfacecolor='r', markeredgecolor='r')\n",
    "plt.xlabel('Number of Neighbors')\n",
    "plt.ylabel('Error Rate')\n",
    "plt.title('Elbow Method')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:12.147838Z",
     "iopub.status.busy": "2020-09-08T03:16:12.146539Z",
     "iopub.status.idle": "2020-09-08T03:16:12.150256Z",
     "shell.execute_reply": "2020-09-08T03:16:12.150944Z"
    },
    "papermill": {
     "duration": 0.048706,
     "end_time": "2020-09-08T03:16:12.151140",
     "exception": false,
     "start_time": "2020-09-08T03:16:12.102434",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:12.242944Z",
     "iopub.status.busy": "2020-09-08T03:16:12.240283Z",
     "iopub.status.idle": "2020-09-08T03:16:12.247941Z",
     "shell.execute_reply": "2020-09-08T03:16:12.248518Z"
    },
    "papermill": {
     "duration": 0.058474,
     "end_time": "2020-09-08T03:16:12.248695",
     "exception": false,
     "start_time": "2020-09-08T03:16:12.190221",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=23)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:12.331602Z",
     "iopub.status.busy": "2020-09-08T03:16:12.330740Z",
     "iopub.status.idle": "2020-09-08T03:16:12.341936Z",
     "shell.execute_reply": "2020-09-08T03:16:12.341116Z"
    },
    "papermill": {
     "duration": 0.056219,
     "end_time": "2020-09-08T03:16:12.342067",
     "exception": false,
     "start_time": "2020-09-08T03:16:12.285848",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "knn_prediction=knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:12.426316Z",
     "iopub.status.busy": "2020-09-08T03:16:12.425449Z",
     "iopub.status.idle": "2020-09-08T03:16:12.440260Z",
     "shell.execute_reply": "2020-09-08T03:16:12.439387Z"
    },
    "papermill": {
     "duration": 0.061825,
     "end_time": "2020-09-08T03:16:12.440401",
     "exception": false,
     "start_time": "2020-09-08T03:16:12.378576",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2 29]\n",
      " [ 0 78]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.06      0.12        31\n",
      "           1       0.73      1.00      0.84        78\n",
      "\n",
      "    accuracy                           0.73       109\n",
      "   macro avg       0.86      0.53      0.48       109\n",
      "weighted avg       0.81      0.73      0.64       109\n",
      "\n",
      "\n",
      "\n",
      "KNN accuracy Accuracy:  0.7339449541284404\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,knn_prediction))\n",
    "print('\\n')\n",
    "print(classification_report(y_test,knn_prediction))\n",
    "print('\\n')\n",
    "print('KNN accuracy Accuracy: ', accuracy_score(y_test,knn_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.035892,
     "end_time": "2020-09-08T03:16:12.515080",
     "exception": false,
     "start_time": "2020-09-08T03:16:12.479188",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### 4-5-SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:12.592711Z",
     "iopub.status.busy": "2020-09-08T03:16:12.591334Z",
     "iopub.status.idle": "2020-09-08T03:16:12.595208Z",
     "shell.execute_reply": "2020-09-08T03:16:12.594346Z"
    },
    "papermill": {
     "duration": 0.04455,
     "end_time": "2020-09-08T03:16:12.595349",
     "exception": false,
     "start_time": "2020-09-08T03:16:12.550799",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "svc=SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:12.685046Z",
     "iopub.status.busy": "2020-09-08T03:16:12.684189Z",
     "iopub.status.idle": "2020-09-08T03:16:12.705415Z",
     "shell.execute_reply": "2020-09-08T03:16:12.704457Z"
    },
    "papermill": {
     "duration": 0.069879,
     "end_time": "2020-09-08T03:16:12.705596",
     "exception": false,
     "start_time": "2020-09-08T03:16:12.635717",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:12.790520Z",
     "iopub.status.busy": "2020-09-08T03:16:12.789661Z",
     "iopub.status.idle": "2020-09-08T03:16:12.794409Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "svc_prediction=svc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "execution": {
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    },
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0 31]\n",
      " [ 1 77]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00        31\n",
      "           1       0.71      0.99      0.83        78\n",
      "\n",
      "    accuracy                           0.71       109\n",
      "   macro avg       0.36      0.49      0.41       109\n",
      "weighted avg       0.51      0.71      0.59       109\n",
      "\n",
      "\n",
      "\n",
      "SVC َAccuracy:  0.7064220183486238\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,svc_prediction))\n",
    "print('\\n')\n",
    "print(classification_report(y_test,svc_prediction))\n",
    "print('\\n')\n",
    "print('SVC َAccuracy: ', accuracy_score(y_test,svc_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-08T03:16:12.977135Z",
     "iopub.status.busy": "2020-09-08T03:16:12.975351Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy:  0.8073394495412844\n",
      "Decision Tree Accuracy:  0.7064220183486238\n",
      "Random Forest Accuracy:  0.7798165137614679\n",
      "KNN Accuracy:  0.7339449541284404\n",
      "SVC Accuracy:  0.7064220183486238\n"
     ]
    }
   ],
   "source": [
    "print('Logistic Regression Accuracy: ', accuracy_score(y_test,lr_prediction))\n",
    "print('Decision Tree Accuracy: ', accuracy_score(y_test,dt_prediction))\n",
    "print('Random Forest Accuracy: ', accuracy_score(y_test,rf_prediction))\n",
    "print('KNN Accuracy: ', accuracy_score(y_test,knn_prediction))\n",
    "print('SVC Accuracy: ', accuracy_score(y_test,svc_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
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     "end_time": "2020-09-08T03:16:13.061982",
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     "start_time": "2020-09-08T03:16:13.022081",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### CONCLUSION\n",
    "\n",
    "The Loan Status is heavily dependent on the Credit History for Predictions.\n",
    "\n",
    "The Logistic Regression algorithm gives us the maximum Accuracy (80%) compared to the other 4 Machine Learning Classification Algorithms."
   ]
  }
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